```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B6"),
    ("x1", "milligrams of vitamin A"),
    ("x2", "milligrams of potassium"),
    ("x3", "milligrams of vitamin C"),
    ("x4", "milligrams of vitamin B7"),
    ("x5", "milligrams of iron")
  ],
  "objective_function": "5.86 * x0 + 3.03 * x1 + 1.04 * x2 + 5.31 * x3 + 6.45 * x4 + 6.45 * x5",
  "constraints": [
    "5.77 * x0 + 9.44 * x2 >= 34",
    "5.77 * x0 + 11.62 * x1 >= 18",
    "5.77 * x0 + 2.99 * x5 >= 32",
    "5.77 * x0 + 4.23 * x4 >= 29",
    "11.62 * x1 + 9.44 * x2 + 2.99 * x5 >= 35",
    "9.44 * x2 + 10.31 * x3 + 2.99 * x5 >= 35",
    "5.77 * x0 + 9.44 * x2 + 2.99 * x5 >= 35",
    "5.77 * x0 + 10.31 * x3 + 4.23 * x4 >= 35",
    "11.62 * x1 + 10.31 * x3 + 4.23 * x4 >= 35",
    "5.77 * x0 + 4.23 * x4 + 2.99 * x5 >= 35",
    "9.87 * x0 + 9.52 * x5 <= 317",
    "1.37 * x1 + 4.72 * x4 <= 117",
    "8.47 * x2 + 9.52 * x5 <= 70",
    "8.47 * x2 + 11.49 * x3 <= 309",
    "8.47 * x2 + 4.72 * x4 <= 163",
    "9.87 * x0 + 8.47 * x2 <= 357",
    "11.49 * x3 + 9.52 * x5 <= 311",
    "9.87 * x0 + 4.72 * x4 <= 77",
    "5.77 * x0 + 10.31 * x3 <= 64",
    "9.44 * x2 + 2.99 * x5 <= 87",
    "11.62 * x1 + 9.44 * x2 <= 136",
    "5.77 * x0 + 11.62 * x1 <= 55",
    "11.62 * x1 + 4.23 * x4 <= 190",
    "5.77 * x0 + 2.99 * x5 <= 133",
    "4.23 * x4 + 2.99 * x5 <= 197",
    "5.77 * x0 + 9.44 * x2 + 10.31 * x3 <= 165",
    "11.62 * x1 + 9.44 * x2 + 2.99 * x5 <= 79",
    "11.62 * x1 + 10.31 * x3 + 2.99 * x5 <= 210",
    "5.77 * x0 + 4.23 * x4 + 2.99 * x5 <= 222",
    "8.47 * x2 + 11.49 * x3 >= 52",
    "4.72 * x4 + 9.52 * x5 >= 26",
    "11.49 * x3 + 9.52 * x5 >= 53",
    "9.87 * x0 + 9.52 * x5 >= 24",
    "9.87 * x0 + 4.72 * x4 >= 39",
    "9.87 * x0 + 8.47 * x2 >= 31",
    "9.87 * x0 + 11.49 * x3 >= 55",
    "1.37 * x1 + 4.72 * x4 >= 60",
    "1.37 * x1 + 8.47 * x2 >= 60",
    "-6 * x1 + 2 * x4 >= 0",
    "2 * x4 - x5 >= 0",
    "5.77 * x0 + 11.62 * x1 + 9.44 * x2 + 10.31 * x3 + 4.23 * x4 + 2.99 * x5 >= 37",
    "9.87 * x0 + 1.37 * x1 + 8.47 * x2 + 11.49 * x3 + 4.72 * x4 + 9.52 * x5 >= 42"

  ]
}
```

```python
import gurobipy as gp

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

# Create variables
vitamin_b6 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b6")
vitamin_a = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_a")
potassium = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="potassium")
vitamin_c = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_c")
vitamin_b7 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b7")
iron = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="iron")


# Set objective function
m.setObjective(5.86 * vitamin_b6 + 3.03 * vitamin_a + 1.04 * potassium + 5.31 * vitamin_c + 6.45 * vitamin_b7 + 6.45 * iron, gp.GRB.MINIMIZE)

# Add constraints

m.addConstr(5.77 * vitamin_b6 + 9.44 * potassium >= 34)
m.addConstr(5.77 * vitamin_b6 + 11.62 * vitamin_a >= 18)
m.addConstr(5.77 * vitamin_b6 + 2.99 * iron >= 32)
m.addConstr(5.77 * vitamin_b6 + 4.23 * vitamin_b7 >= 29)
m.addConstr(11.62 * vitamin_a + 9.44 * potassium + 2.99 * iron >= 35)
m.addConstr(9.44 * potassium + 10.31 * vitamin_c + 2.99 * iron >= 35)
m.addConstr(5.77 * vitamin_b6 + 9.44 * potassium + 2.99 * iron >= 35)
m.addConstr(5.77 * vitamin_b6 + 10.31 * vitamin_c + 4.23 * vitamin_b7 >= 35)
m.addConstr(11.62 * vitamin_a + 10.31 * vitamin_c + 4.23 * vitamin_b7 >= 35)
m.addConstr(5.77 * vitamin_b6 + 4.23 * vitamin_b7 + 2.99 * iron >= 35)

# ... (rest of the constraints from the JSON "constraints" field)

m.addConstr(9.87 * vitamin_b6 + 9.52 * iron <= 317)
m.addConstr(1.37 * vitamin_a + 4.72 * vitamin_b7 <= 117)
# ... (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("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```