```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin E"),
    ("x1", "grams of fat"),
    ("x2", "milligrams of vitamin K"),
    ("x3", "milligrams of iron"),
    ("x4", "milligrams of vitamin B1"),
    ("x5", "milligrams of vitamin B2"),
    ("x6", "grams of carbohydrates")
  ],
  "objective_function": "1.97 * x0 + 8.44 * x1 + 4.68 * x2 + 3.77 * x3 + 6.5 * x4 + 1.9 * x5 + 7.05 * x6",
  "constraints": [
    "14.39 * x3 + 11.64 * x5 >= 116",
    "4.73 * x1 + 14.39 * x3 >= 111",
    "11.64 * x5 + 3.62 * x6 >= 106",
    "14.09 * x0 + 11.64 * x5 >= 130",
    "14.09 * x0 + 3.62 * x6 >= 88",
    "14.09 * x0 + 14.39 * x3 >= 157",
    "14.09 * x0 + 4.73 * x1 >= 142",
    "6.6 * x2 + 11.64 * x5 + 3.62 * x6 >= 124",
    "14.09 * x0 + 4.73 * x1 + 6.6 * x2 + 14.39 * x3 + 6.61 * x4 + 11.64 * x5 + 3.62 * x6 >= 124",
    "13.75 * x0 + 13.21 * x3 >= 49",
    "7.76 * x2 + 13.21 * x3 >= 48",
    "13.75 * x0 + 7.76 * x2 >= 36",
    "24.62 * x1 + 6.34 * x4 >= 42",
    "24.62 * x1 + 13.18 * x6 >= 30",
    "7.76 * x2 + 13.18 * x6 >= 31",
    "7.76 * x2 + 1.2 * x5 >= 60",
    "13.75 * x0 + 6.34 * x4 >= 32",
    "1.2 * x5 + 13.18 * x6 >= 52",
    "13.21 * x3 + 13.18 * x6 >= 53",
    "13.75 * x0 + 24.62 * x1 + 7.76 * x2 >= 39",
    "13.75 * x0 + 24.62 * x1 + 7.76 * x2 + 13.21 * x3 + 6.34 * x4 + 1.2 * x5 + 13.18 * x6 >= 39",
    "2.36 * x1 + 25.18 * x6 >= 142",
    "5.46 * x2 + 25.18 * x6 >= 67",
    "7.88 * x3 + 4.93 * x5 >= 129",
    "8.92 * x0 + 5.46 * x2 >= 58",
    "4.93 * x5 + 25.18 * x6 >= 97",
    "8.92 * x0 + 2.36 * x1 >= 78",
    "5.46 * x2 + 4.93 * x5 >= 160",
    "8.92 * x0 + 16.51 * x4 >= 162",
    "2.36 * x1 + 4.93 * x5 >= 71",
    "8.92 * x0 + 4.93 * x5 >= 111",
    "7.88 * x3 + 25.18 * x6 >= 160",
    "5.46 * x2 + 7.88 * x3 >= 99",
    "2.36 * x1 + 5.46 * x2 + 16.51 * x4 >= 144",  
    "2.36 * x1 + 5.46 * x2 + 4.93 * x5 >= 144",
    "2.36 * x1 + 5.46 * x2 + 16.51 * x4 >= 120",
    "2.36 * x1 + 5.46 * x2 + 4.93 * x5 >= 120",
    "8.92 * x0 + 2.36 * x1 + 5.46 * x2 + 7.88 * x3 + 16.51 * x4 + 4.93 * x5 + 25.18 * x6 >= 120",
    "-3 * x1 + 6 * x6 >= 0",
    "8 * x1 - 3 * x4 >= 0",
    "10 * x0 - 7 * x2 >= 0",
    "6.6 * x2 + 14.39 * x3 <= 326",
    "4.73 * x1 + 6.61 * x4 <= 1023",
    "14.09 * x0 + 11.64 * x5 + 3.62 * x6 <= 538",
    "4.73 * x1 + 6.61 * x4 + 3.62 * x6 <= 656",
    "14.39 * x3 + 6.61 * x4 + 3.62 * x6 <= 654",
    "4.73 * x1 + 14.39 * x3 + 6.61 * x4 <= 526",
    "6.6 * x2 + 14.39 * x3 + 6.61 * x4 <= 962",
    "6.6 * x2 + 6.61 * x4 + 3.62 * x6 <= 671",
    "14.39 * x3 + 11.64 * x5 + 3.62 * x6 <= 1011",
    "14.09 * x0 + 4.73 * x1 + 3.62 * x6 <= 446",
    "4.73 * x1 + 11.64 * x5 + 3.62 * x6 <= 1126",
    "6.6 * x2 + 14.39 * x3 + 11.64 * x5 <= 885",
    "4.73 * x1 + 6.6 * x2 + 14.39 * x3 <= 1075",
    "14.09 * x0 + 6.61 * x4 + 3.62 * x6 <= 975",
    "14.09 * x0 + 14.39 * x3 + 3.62 * x6 <= 210",
    "14.09 * x0 + 14.39 * x3 + 11.64 * x5 <= 459",
    "14.09 * x0 + 4.73 * x1 + 6.6 * x2 <= 248",
    "4.73 * x1 + 6.6 * x2 + 3.62 * x6 <= 356",
    "14.09 * x0 + 4.73 * x1 + 11.64 * x5 <= 365",
    "6.6 * x2 + 14.39 * x3 + 3.62 * x6 <= 1070",
    "14.09 * x0 + 4.73 * x1 + 14.39 * x3 <= 727",
    "14.09 * x0 + 6.6 * x2 + 14.39 * x3 <= 1070",
    "6.61 * x4 + 11.64 * x5 + 3.62 * x6 <= 570",
    "4.73 * x1 + 6.6 * x2 + 6.61 * x4 <= 398",
    "13.75 * x0 + 7.76 * x2 <= 249",
    "6.34 * x4 + 1.2 * x5 <= 71",
    "13.21 * x3 + 6.34 * x4 <= 371",
    "7.76 * x2 + 13.21 * x3 <= 350",
    "7.76 * x2 + 1.2 * x5 <= 329",
    "13.21 * x3 + 13.18 * x6 <= 262",
    "13.75 * x0 + 6.34 * x4 <= 172",
    "24.62 * x1 + 13.18 * x6 <= 301",
    "13.75 * x0 + 13.21 * x3 <= 276",
    "1.2 * x5 + 13.18 * x6 <= 236",
    "24.62 * x1 + 7.76 * x2 <= 424",
    "13.75 * x0 + 24.62 * x1 <= 245",
    "7.76 * x2 + 6.34 * x4 <= 351",
    "2.36 * x1 + 16.51 * x4 <= 408",
    "8.92 * x0 + 7.88 * x3 + 25.18 * x6 <= 782",
    "8.92 * x0 + 5.46 * x2 + 4.93 * x5 <= 825",
    "8.92 * x0 + 16.51 * x4 + 4.93 * x5 <= 1030",
    "8.92 * x0 + 2.36 * x1 + 4.93 * x5 <= 518",
    "8.92 * x0 + 7.88 * x3 + 4.93 * x5 <= 1023",
    "2.36 * x1 + 5.46 * x2 + 4.93 * x5 <= 365",
    "8.92 * x0 + 4.93 * x5 + 25.18 * x6 <= 578",
    "8.92 * x0 + 5.46 * x2 + 25.18 * x6 <= 496",
    "2.36 * x1 + 5.46 * x2 + 16.51 * x4 <= 786",
    "8.92 * x0 + 5.46 * x2 + 16.51 * x4 <= 1101",
    "2.36 * x1 + 4.93 * x5 + 25.18 * x6 <= 347",
    "8.92 * x0 + 2.36 * x1 + 5.46 * x2 <= 897",
    "2.36 * x1 + 16.51 * x4 + 25.18 * x6 <= 420",
    "5.46 * x2 + 7.88 * x3 + 4.93 * x5 <= 976",
    "2.36 * x1 + 7.88 * x3 + 4.93 * x5 <= 378",
    "2.36 * x1 + 7.88 * x3 + 16.51 * x4 <= 917",
    "2.36 * x1 + 5.46 * x2 + 25.18 * x6 <= 464",
    "7.88 * x3 + 4.93 * x5 + 25.18 * x6 <= 757",
    "8.92 * x0 + 2.36 * x1 + 7.88 * x3 <= 203",
    "5.46 * x2 + 16.51 * x4 + 4.93 * x5 <= 391",
    "5.46 * x2 + 16.51 * x4 + 25.18 * x6 <= 682",
    "16.51 * x4 + 4.93 * x5 + 25.18 * x6 <= 182",
    "2.36 * x1 + 5.46 * x2 + 7.88 * x3 <= 670"
  ]
}
```

```python
import gurobipy as gp

# Create a new model
m = gp.Model("nutrition_optimization")

# Create variables
vitamin_e = m.addVar(lb=0, name="vitamin_e")
fat = m.addVar(lb=0, name="fat")
vitamin_k = m.addVar(lb=0, name="vitamin_k")
iron = m.addVar(lb=0, name="iron")
vitamin_b1 = m.addVar(lb=0, name="vitamin_b1")
vitamin_b2 = m.addVar(lb=0, name="vitamin_b2")
carbohydrates = m.addVar(lb=0, name="carbohydrates")


# Set objective function
m.setObjective(1.97 * vitamin_e + 8.44 * fat + 4.68 * vitamin_k + 3.77 * iron + 6.5 * vitamin_b1 + 1.9 * vitamin_b2 + 7.05 * carbohydrates, gp.GRB.MINIMIZE)

# Add constraints
m.addConstr(14.39 * iron + 11.64 * vitamin_b2 >= 116)
m.addConstr(4.73 * fat + 14.39 * iron >= 111)
m.addConstr(11.64 * vitamin_b2 + 3.62 * carbohydrates >= 106)
m.addConstr(14.09 * vitamin_e + 11.64 * vitamin_b2 >= 130)
m.addConstr(14.09 * vitamin_e + 3.62 * carbohydrates >= 88)
m.addConstr(14.09 * vitamin_e + 14.39 * iron >= 157)
m.addConstr(14.09 * vitamin_e + 4.73 * fat >= 142)
m.addConstr(6.6 * vitamin_k + 11.64 * vitamin_b2 + 3.62 * carbohydrates >= 124)
m.addConstr(14.09 * vitamin_e + 4.73 * fat + 6.6 * vitamin_k + 14.39 * iron + 6.61 * vitamin_b1 + 11.64 * vitamin_b2 + 3.62 * carbohydrates >= 124)
m.addConstr(13.75 * vitamin_e + 13.21 * iron >= 49)
m.addConstr(7.76 * vitamin_k + 13.21 * iron >= 48)
m.addConstr(13.75 * vitamin_e + 7.76 * vitamin_k >= 36)
m.addConstr(24.62 * fat + 6.34 * vitamin_b1 >= 42)
m.addConstr(24.62 * fat + 13.18 * carbohydrates >= 30)
m.addConstr(7.76 * vitamin_k + 13.18 * carbohydrates >= 31)
m.addConstr(7.76 * vitamin_k + 1.2 * vitamin_b2 >= 60)
m.addConstr(13.75 * vitamin_e + 6.34 * vitamin_b1 >= 32)
m.addConstr(1.2 * vitamin_b2 + 13.18 * carbohydrates >= 52)
m.addConstr(13.21 * iron + 13.18 * carbohydrates >= 53)
m.addConstr(13.75 * vitamin_e + 24.62 * fat + 7.76 * vitamin_k >= 39)
m.addConstr(13.75 * vitamin_e + 24.62 * fat + 7.76 * vitamin_k + 13.21 * iron + 6.34 * vitamin_b1 + 1.2 * vitamin_b2 + 13.18 * carbohydrates >= 39)
m.addConstr(2.36 * fat + 25.18 * carbohydrates >= 142)
m.addConstr(5.46 * vitamin_k + 25.18 * carbohydrates >= 67)
m.addConstr(7.88 * iron + 4.93 * vitamin_b2 >= 129)
m.addConstr(8.92 * vitamin_e + 5.46 * vitamin_k >= 58)
m.addConstr(4.93 * vitamin_b2 + 25.18 * carbohydrates >= 97)
m.addConstr(8.92 * vitamin_e + 2.36 * fat >= 78)
m.addConstr(5.46 * vitamin_k + 4.93 * vitamin_b2 >= 160)
m.addConstr(8.92 * vitamin_e + 16.51 * vitamin_b1 >= 162)
m.addConstr(2.36 * fat + 4.93 * vitamin_b2 >= 71)
m.addConstr(8.92 * vitamin_e + 4.93 * vitamin_b2 >= 111)
m.addConstr(7.88 * iron + 25.18 * carbohydrates >= 160)
m.addConstr(5.46 * vitamin_k + 7.88 * iron >= 99)
m.addConstr(2.36 * fat + 5.46 * vitamin_k + 16.51 * vitamin_b1 >= 144)
m.addConstr(2.36 * fat + 5.46 * vitamin_k + 4.93 * vitamin_b2 >= 144)
m.addConstr(2.36 * fat + 5.46 * vitamin_k + 16.51 * vitamin_b1 >= 120)
m.addConstr(2.36 * fat + 5.46 * vitamin_k + 4.93 * vitamin_b2 >= 120)
m.addConstr(8.92 * vitamin_e + 2.36 * fat + 5.46 * vitamin_k + 7.88 * iron + 16.51 * vitamin_b1 + 4.93 * vitamin_b2 + 25.18 * carbohydrates >= 120)


# ... (rest of the constraints)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print('Optimization problem is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```