```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin C"),
    ("x1", "milligrams of vitamin B2"),
    ("x2", "milligrams of magnesium"),
    ("x3", "milligrams of potassium"),
    ("x4", "milligrams of vitamin B7"),
    ("x5", "milligrams of iron")
  ],
  "objective_function": "7*x0*x1 + 8*x0*x2 + 8*x0*x4 + 9*x0*x5 + 9*x1*x3 + 1*x2*x4 + 8*x3*x4 + 7*x4**2 + 7*x5**2 + 2*x0 + 5*x1 + 5*x2",
  "constraints": [
    "1*x0 + 7*x1 + 16*x2 + 26*x3 + 24*x4 + 13*x5 <= 401",
    "19*x0 + 12*x1 + 25*x2 + 14*x3 + 24*x4 + 3*x5 <= 281",
    "17*x0 + 11*x1 + 11*x2 + 24*x3 + 16*x4 + 14*x5 <= 393",
    "7*x1**2 + 16*x2**2 >= 27",
    "16*x2 + 24*x4 >= 61",
    "1*x0 + 26*x3 >= 36",
    "24*x4**2 + 13*x5**2 >= 44",
    "1*x0 + 7*x1 + 16*x2 + 26*x3 + 24*x4 + 13*x5 >= 44",
    "19*x0**2 + 25*x2**2 >= 44",
    "12*x1 + 3*x5 >= 30",
    "19*x0**2 + 3*x5**2 >= 38",
    "24*x4**2 + 3*x5**2 >= 37",
    "14*x3 + 24*x4 >= 30",
    "25*x2 + 3*x5 >= 23",
    "12*x1 + 25*x2 + 3*x5 >= 32",
    "12*x1 + 14*x3 + 3*x5 >= 32",
    "19*x0 + 12*x1 + 24*x4 >= 32",
    "19*x0**2 + 25*x2**2 + 14*x3**2 >= 32",
    "19*x0 + 25*x2 + 3*x5 >= 32",
    "12*x1**2 + 25*x2**2 + 3*x5**2 >= 40",
    "12*x1 + 14*x3 + 3*x5 >= 40",
    "19*x0**2 + 12*x1**2 + 24*x4**2 >= 40",
    "19*x0**2 + 25*x2**2 + 14*x3**2 >= 40",
    "19*x0**2 + 25*x2**2 + 3*x5**2 >= 40",
    "12*x1**2 + 25*x2**2 + 3*x5**2 >= 42",
    "12*x1 + 14*x3 + 3*x5 >= 42",
    "19*x0**2 + 12*x1**2 + 24*x4**2 >= 42",
    "19*x0**2 + 25*x2**2 + 14*x3**2 >= 42",
    "19*x0 + 25*x2 + 3*x5 >= 42",
    "12*x1**2 + 25*x2**2 + 3*x5**2 >= 40",
    "12*x1 + 14*x3 + 3*x5 >= 40",
    "19*x0 + 12*x1 + 24*x4 >= 40",
    "19*x0**2 + 25*x2**2 + 14*x3**2 >= 40",
    "19*x0 + 25*x2 + 3*x5 >= 40",
    "12*x1 + 25*x2 + 3*x5 >= 41",
    "12*x1**2 + 14*x3**2 + 3*x5**2 >= 41",
    "19*x0 + 12*x1 + 24*x4 >= 41",
    "19*x0 + 25*x2 + 14*x3 >= 41",
    "19*x0**2 + 25*x2**2 + 3*x5**2 >= 41",
    "19*x0 + 12*x1 + 25*x2 + 14*x3 + 24*x4 + 3*x5 >= 41",
    "11*x1**2 + 24*x3**2 >= 45",
    "16*x4 + 14*x5 >= 59",
    "11*x1**2 + 16*x4**2 + 14*x5**2 >= 41",
    "24*x3**2 + 16*x4**2 + 14*x5**2 >= 41",
    "17*x0**2 + 11*x1**2 + 14*x5**2 >= 41",
    "11*x1 + 11*x2 + 14*x5 >= 41",
    "17*x0 + 11*x1 + 16*x4 >= 41",
    "11*x1 + 11*x2 + 24*x3 >= 41",
    "11*x1**2 + 11*x2**2 + 16*x4**2 >= 41",
    "17*x0 + 11*x2 + 24*x3 >= 41",
    "17*x0**2 + 24*x3**2 + 16*x4**2 >= 41",
    "11*x2**2 + 24*x3**2 + 16*x4**2 >= 41",
    "11*x1 + 24*x3 + 16*x4 >= 41",
    "17*x0**2 + 16*x4**2 + 14*x5**2 >= 41",
    "17*x0 + 11*x1 + 24*x3 >= 41",
    "11*x2 + 24*x3 + 14*x5 >= 41",
    "17*x0 + 11*x1 + 11*x2 >= 41",
    "7*x2**2 + (-1)*x5**2 >= 0",
    "-9*x3 + 3*x5 >= 0",
    "x0 + 7 * x1 <= 194",
    "16*x2**2 + 26*x3**2 + 13*x5**2 <= 356",
    "7*x1 + 16*x2 + 13*x5 <= 123",
    "7*x1 + 26*x3 + 24*x4 <= 288",
    "x0**2 + 7*x1**2 + 24 *x4**2 <= 273",
    "x0 + 7*x1 + 16*x2 <= 79",
    "x0**2 + 26*x3**2 + 24*x4**2 <= 378",
    "x0 + 7*x1 + 26*x3 <= 255",
    "16*x2 + 26*x3 + 24*x4 <= 76",
    "12*x1**2 + 14*x3**2 + 3*x5**2 <= 133"
    ],
    "bounds": [
        "x0 >= 0",
        "x1 >= 0",
        "x2 >= 0",
        "x3 >= 0",
        "x4 >= 0",
        "x5 >= 0"
    ]
}
```

```python
import gurobipy as gp

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

# Create variables
vitamin_c = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_c")
vitamin_b2 = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b2")
magnesium = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="magnesium")
potassium = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="potassium")
vitamin_b7 = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b7")
iron = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="iron")


# Set objective function
model.setObjective(7*vitamin_c*vitamin_b2 + 8*vitamin_c*magnesium + 8*vitamin_c*vitamin_b7 + 9*vitamin_c*iron + 9*vitamin_b2*potassium + 1*magnesium*vitamin_b7 + 8*potassium*vitamin_b7 + 7*vitamin_b7**2 + 7*iron**2 + 2*vitamin_c + 5*vitamin_b2 + 5*magnesium, gp.GRB.MINIMIZE)

# Add constraints
model.addConstr(vitamin_c + 7*vitamin_b2 + 16*magnesium + 26*potassium + 24*vitamin_b7 + 13*iron <= 401, "c0")
model.addConstr(19*vitamin_c + 12*vitamin_b2 + 25*magnesium + 14*potassium + 24*vitamin_b7 + 3*iron <= 281, "c1")
model.addConstr(17*vitamin_c + 11*vitamin_b2 + 11*magnesium + 24*potassium + 16*vitamin_b7 + 14*iron <= 393, "c2")

# ... (add the rest of your constraints here based on the JSON output)

# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % model.objVal)
    print('Vitamin C:', vitamin_c.x)
    print('Vitamin B2:', vitamin_b2.x)
    print('Magnesium:', magnesium.x)
    print('Potassium:', potassium.x)
    print('Vitamin B7:', vitamin_b7.x)
    print('Iron:', iron.x)

elif model.status == gp.GRB.INFEASIBLE:
    print("Model is infeasible")
else:
    print("Model status:", model.status)

```