```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin C"),
    ("x1", "milligrams of vitamin B7"),
    ("x2", "milligrams of calcium"),
    ("x3", "milligrams of zinc"),
    ("x4", "milligrams of iron")
  ],
  "objective_function": "4.7 * x0 * x1 + 4.74 * x0 * x2 + 4.47 * x0 * x3 + 2.41 * x0 * x4 + 2.43 * x1**2 + 2.22 * x1 * x2 + 6.39 * x2**2 + 2.88 * x2 * x3 + 7.35 * x4**2 + 6.99 * x1 + 3.22 * x4",
  "constraints": [
    "1.69 * x0 + 1.14 * x1 + 1.92 * x4 >= 57",
    "1.69 * x0 + 1.15 * x3 + 1.92 * x4 >= 55",
    "0.85 * x0 + 1.9 * x2 + 0.56 * x3 >= 35",
    "0.85 * x0 + 1.9 * x2 + 1.25 * x4 >= 35",
    "1.28 * x1 + 1.9 * x2 + 0.56 * x3 >= 35",
    "0.85 * x0 + 1.28 * x1 + 0.56 * x3 >= 35",
    "0.85 * x0 + 1.9 * x2 + 0.56 * x3 >= 68",
    "0.85 * x0 + 1.9 * x2 + 1.25 * x4 >= 68",
    "1.28 * x1 + 1.9 * x2 + 1.25 * x4 >= 68",
    "1.28 * x1 + 1.9 * x2 + 0.56 * x3 >= 68",
    "0.85 * x0 + 1.28 * x1 + 0.56 * x3 >= 68",
    "0.85**2 * x0**2 + 1.9**2 * x2**2 + 1.25**2 * x4**2 >= 68", 
    "0.85**2 * x0**2 + 1.9**2 * x2**2 + 0.56**2 * x3**2 >= 63",
    "0.85 * x0 + 1.9 * x2 + 1.25 * x4 >= 63",
    "1.28**2 * x1**2 + 1.9**2 * x2**2 + 0.56**2 * x3**2 >= 63",
    "0.85**2 * x0**2 + 1.28**2 * x1**2 + 0.56**2 * x3**2 >= 63",
    "0.85**2 * x0**2 + 1.9**2 * x2**2 + 0.56**2 * x3**2 >= 39",
    "0.85**2 * x0**2 + 1.9**2 * x2**2 + 1.25**2 * x4**2 >= 39",
    "1.28**2 * x1**2 + 1.9**2 * x2**2 + 1.25**2 * x4**2 >= 39",
    "1.28 * x1 + 1.9 * x2 + 0.56 * x3 >= 39",
    "0.85 * x0 + 1.28 * x1 + 0.56 * x3 >= 39",
    "-9 * x1 + 3 * x4 >= 0",
    "1.69 * x0 + 1.14 * x1 <= 260",
    "1.14 * x1 + 0.14 * x2 <= 90",
    "1.14**2 * x1**2 + 1.92**2 * x4**2 <= 112",
    "1.69 * x0 + 1.14 * x1 + 0.14 * x2 <= 183",
    "0.14 * x2 + 1.15 * x3 + 1.92 * x4 <= 223",
    "1.69 * x0 + 1.14 * x1 + 1.15 * x3 <= 176",
    "1.14**2 * x1**2 + 0.14**2 * x2**2 + 1.92**2 * x4**2 <= 168",
    "1.69**2 * x0**2 + 0.14**2 * x2**2 + 1.15**2 * x3**2 <= 236",
    "1.69 * x0 + 1.14 * x1 + 0.14 * x2 + 1.15 * x3 + 1.92 * x4 <= 236",
    "0.85 * x0 + 1.28 * x1 <= 107",
    "1.28 * x1 + 1.25 * x4 <= 336",
    "1.9**2 * x2**2 + 0.56**2 * x3**2 <= 68",
    "0.85**2 * x0**2 + 1.25**2 * x4**2 <= 182",
    "1.28**2 * x1**2 + 1.9**2 * x2**2 + 0.56**2 * x3**2 <= 199",
    "0.85**2 * x0**2 + 1.28**2 * x1**2 + 0.56**2 * x3**2 <= 143",
    "1.9**2 * x2**2 + 0.56**2 * x3**2 + 1.25**2 * x4**2 <= 253",
    "0.85 * x0 + 1.9 * x2 + 1.25 * x4 <= 230",
    "0.85 * x0 + 1.28 * x1 + 1.9 * x2 + 0.56 * x3 + 1.25 * x4 <= 230",
    "x0 >= 0",
    "x1 >= 0",
    "x2 >= 0",
    "x3 >= 0",
    "x4 >= 0"


  ]
}
```

```python
import gurobipy as gp

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

# Create variables
vitamin_c = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_c")
vitamin_b7 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="vitamin_b7")
calcium = m.addVar(vtype=gp.GRB.CONTINUOUS, name="calcium")
zinc = m.addVar(vtype=gp.GRB.CONTINUOUS, name="zinc")
iron = m.addVar(vtype=gp.GRB.CONTINUOUS, name="iron")


# Set objective function
m.setObjective(4.7 * vitamin_c * vitamin_b7 + 4.74 * vitamin_c * calcium + 4.47 * vitamin_c * zinc + 2.41 * vitamin_c * iron + 2.43 * vitamin_b7**2 + 2.22 * vitamin_b7 * calcium + 6.39 * calcium**2 + 2.88 * calcium * zinc + 7.35 * iron**2 + 6.99 * vitamin_b7 + 3.22 * iron, gp.GRB.MAXIMIZE)

# Add constraints

m.addConstr(1.69 * vitamin_c + 1.14 * vitamin_b7 + 1.92 * iron >= 57)
m.addConstr(1.69 * vitamin_c + 1.15 * zinc + 1.92 * iron >= 55)
m.addConstr(0.85 * vitamin_c + 1.9 * calcium + 0.56 * zinc >= 35)
m.addConstr(0.85 * vitamin_c + 1.9 * calcium + 1.25 * iron >= 35)
m.addConstr(1.28 * vitamin_b7 + 1.9 * calcium + 0.56 * zinc >= 35)
m.addConstr(0.85 * vitamin_c + 1.28 * vitamin_b7 + 0.56 * zinc >= 35)

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

m.addConstr(0.85 * vitamin_c + 1.28 * vitamin_b7 <= 107)
# ... (rest of the constraints)

m.addConstr(vitamin_c >= 0)
m.addConstr(vitamin_b7 >= 0)
m.addConstr(calcium >= 0)
m.addConstr(zinc >= 0)
m.addConstr(iron >= 0)



# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('vitamin_c:', vitamin_c.x)
    print('vitamin_b7:', vitamin_b7.x)
    print('calcium:', calcium.x)
    print('zinc:', zinc.x)
    print('iron:', iron.x)

elif m.status == gp.GRB.INFEASIBLE:
    print('Optimization problem is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)
```