```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin C"),
    ("x1", "milligrams of vitamin B6"),
    ("x2", "milligrams of vitamin B7"),
    ("x3", "milligrams of vitamin B9"),
    ("x4", "grams of fat"),
    ("x5", "milligrams of vitamin B2")
  ],
  "objective_function": "3.73*x0 + 2.04*x1 + 4.59*x2 + 3.6*x3 + 6.06*x4 + 8.56*x5",
  "constraints": [
    "10*x1 + 14*x5 >= 28",
    "10*x1 + 17*x2 >= 32",
    "17*x2 + 14*x5 >= 28",
    "16*x0 + 17*x2 >= 19",
    "15*x3 + 3*x4 >= 13",
    "10*x1 + 15*x3 >= 15",
    "3*x4 + 14*x5 >= 29",
    "15*x3 + 14*x5 >= 22",
    "17*x2 + 3*x4 >= 32",
    "16*x0 + 14*x5 >= 16",
    "10*x1 + 17*x2 + 14*x5 >= 21",
    "16*x0 + 10*x1 + 3*x4 >= 21",
    "10*x1 + 15*x3 + 14*x5 >= 21",
    "16*x0 + 17*x2 + 14*x5 >= 21",
    "16*x0 + 3*x4 + 14*x5 >= 21",
    "16*x0 + 17*x2 + 3*x4 >= 21",
    "10*x1 + 15*x3 + 3*x4 >= 21",
    "10*x1 + 17*x2 + 14*x5 >= 18",
    "16*x0 + 10*x1 + 3*x4 >= 18",
    "10*x1 + 15*x3 + 14*x5 >= 18",
    "16*x0 + 17*x2 + 14*x5 >= 18",
    "16*x0 + 3*x4 + 14*x5 >= 18",
    "16*x0 + 17*x2 + 3*x4 >= 18",
    "10*x1 + 15*x3 + 3*x4 >= 18",

    "15*x2 + 27*x3 >= 33",
    "16*x4 + 13*x5 >= 44",
    "4*x1 + 27*x3 + 16*x4 >= 25",
    "27*x3 + 16*x4 + 13*x5 >= 25",
    "15*x2 + 16*x4 + 13*x5 >= 25",
    "7*x0 + 4*x1 + 15*x2 >= 25",
    "7*x0 + 27*x3 + 16*x4 >= 25",
    "7*x0 + 15*x2 + 27*x3 >= 25",
    "4*x1 + 16*x4 + 13*x5 >= 25",
    "7*x0 + 16*x4 + 13*x5 >= 25",
    "16*x0 + 15*x3 <= 193",
    "7*x0 + 27*x3 <= 282"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
vitamin_c = m.addVar(lb=0, name="vitamin_c")
vitamin_b6 = m.addVar(lb=0, name="vitamin_b6")
vitamin_b7 = m.addVar(lb=0, name="vitamin_b7")
vitamin_b9 = m.addVar(lb=0, name="vitamin_b9")
fat = m.addVar(lb=0, name="fat")
vitamin_b2 = m.addVar(lb=0, name="vitamin_b2")


# Set objective function
m.setObjective(3.73 * vitamin_c + 2.04 * vitamin_b6 + 4.59 * vitamin_b7 + 3.6 * vitamin_b9 + 6.06 * fat + 8.56 * vitamin_b2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(10 * vitamin_b6 + 14 * vitamin_b2 >= 28)
m.addConstr(10 * vitamin_b6 + 17 * vitamin_b7 >= 32)
# ... (add all other constraints similarly based on the JSON representation)
m.addConstr(16 * vitamin_c + 15 * vitamin_b9 <= 193)
m.addConstr(7 * vitamin_c + 27 * vitamin_b9 <= 282)


# 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)

```