```json
{
  "sym_variables": [
    ("x0", "milligrams of potassium"),
    ("x1", "milligrams of vitamin B5"),
    ("x2", "milligrams of zinc"),
    ("x3", "milligrams of vitamin E"),
    ("x4", "milligrams of vitamin A"),
    ("x5", "milligrams of calcium"),
    ("x6", "grams of fiber")
  ],
  "objective_function": "8.94 * x0 + 8.74 * x1 + 4.2 * x2 + 4.2 * x3 + 3.65 * x4 + 5.29 * x5 + 4.49 * x6",
  "constraints": [
    "14 * x0 + 16 * x1 + 2 * x2 + 10 * x3 + 12 * x4 + 16 * x5 + 2 * x6 <= 574",
    "7 * x0 + 11 * x1 + 16 * x2 + 15 * x3 + 1 * x4 + 4 * x5 + 11 * x6 <= 686",
    "16 * x1 + 2 * x2 >= 29",
    "16 * x5 + 2 * x6 >= 28",
    "10 * x3 + 12 * x4 >= 46",
    "16 * x1 + 16 * x5 >= 35",
    "14 * x0 + 2 * x6 >= 79",
    "14 * x0 + 12 * x4 >= 68",
    "16 * x1 + 2 * x6 >= 52",
    "14 * x0 + 2 * x2 >= 70",
    "12 * x4 + 16 * x5 >= 53",
    "2 * x2 + 12 * x4 >= 79",
    "16 * x1 + 10 * x3 >= 80",
    "2 * x2 + 16 * x5 >= 65",
    "14 * x0 + 16 * x1 + 16 * x5 >= 60",
    "14 * x0 + 16 * x5 + 2 * x6 >= 60",
    "10 * x3 + 12 * x4 + 16 * x5 >= 60",
    "14 * x0 + 12 * x4 + 2 * x6 >= 60",
    "2 * x2 + 10 * x3 + 16 * x5 >= 60",
    "16 * x1 + 10 * x3 + 12 * x4 >= 60",
    "16 * x1 + 16 * x5 + 2 * x6 >= 60",
    "16 * x1 + 2 * x2 + 12 * x4 >= 60",
    "2 * x2 + 12 * x4 + 16 * x5 >= 60",
    "14 * x0 + 2 * x2 + 2 * x6 >= 60",
    "7 * x0 + 15 * x3 + 4 * x5 >= 68",
    "11 * x1 + 1 * x4 + 11 * x6 >= 68",
    "10 * x3 + 4 * x5 + 11 * x6 >= 68",
    "10 * x3 + 4 * x5 <= 185",
    "14 * x0 + 16 * x1 <= 208",
    "16 * x1 + 2 * x2 <= 454",
    "16 * x1 + 12 * x4 <= 382",
    "12 * x4 + 16 * x5 <= 99",
    "14 * x0 + 2 * x2 + 2 * x6 <= 275",
    "7 * x0 + 11 * x1 <= 274",
    "7 * x0 + 1 * x4 <= 674",
    "7 * x0 + 4 * x5 <= 670",
    "7 * x0 + 15 * x3 <= 223",
    "11 * x1 + 16 * x2 <= 653",
    "16 * x2 + 4 * x5 <= 479",
    "1 * x4 + 4 * x5 <= 281",
    "11 * x1 + 1 * x4 <= 101",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0",
    "x4 >= 0",
    "x5 >= 0",
    "x6 >= 0"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
potassium = m.addVar(vtype=gp.GRB.INTEGER, name="potassium")
vitamin_b5 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="vitamin_b5")
zinc = m.addVar(vtype=gp.GRB.INTEGER, name="zinc")
vitamin_e = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_e")
vitamin_a = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_a")
calcium = m.addVar(vtype=gp.GRB.INTEGER, name="calcium")
fiber = m.addVar(vtype=gp.GRB.INTEGER, name="fiber")


# Set objective function
m.setObjective(8.94 * potassium + 8.74 * vitamin_b5 + 4.2 * zinc + 4.2 * vitamin_e + 3.65 * vitamin_a + 5.29 * calcium + 4.49 * fiber, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(14 * potassium + 16 * vitamin_b5 + 2 * zinc + 10 * vitamin_e + 12 * vitamin_a + 16 * calcium + 2 * fiber <= 574, "r0")
m.addConstr(7 * potassium + 11 * vitamin_b5 + 16 * zinc + 15 * vitamin_e + 1 * vitamin_a + 4 * calcium + 11 * fiber <= 686, "r1")

# ... (add all other constraints similarly, replacing variable names)

# Add non-negativity constraints explicitly
m.addConstr(potassium >= 0)
m.addConstr(vitamin_b5 >= 0)
m.addConstr(zinc >= 0)
m.addConstr(vitamin_e >= 0)
m.addConstr(vitamin_a >= 0)
m.addConstr(calcium >= 0)
m.addConstr(fiber >= 0)


# 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("Model is infeasible")
else:
    print("Optimization ended with status %d" % m.status)

```