```json
{
  "sym_variables": [
    ("x0", "milligrams of iron"),
    ("x1", "milligrams of vitamin E"),
    ("x2", "grams of fat"),
    ("x3", "milligrams of magnesium"),
    ("x4", "milligrams of vitamin B4")
  ],
  "objective_function": "8.26*x0*x1 + 9.21*x0*x3 + 9.42*x0*x4 + 6.85*x1**2 + 5.88*x1*x2 + 7.54*x1*x3 + 1.88*x1*x4 + 8.33*x2*x4 + 9.58*x3**2 + 9.21*x4**2 + 9.35*x1 + 3.49*x2 + 5.72*x3 + 5.87*x4",
  "constraints": [
    "0.43*x0 <= 174",
    "0.21*x0 <= 87",
    "0.3*x0 <= 120",
    "0.5*x0 <= 132",
    "0.3*x0 <= 93",
    "0.89*x1 <= 174",
    "0.42*x1 <= 87",
    "0.86*x1 <= 120",
    "0.37*x1 <= 132",
    "0.74*x1 <= 93",
    "0.8*x2 <= 174",
    "0.09*x2 <= 87",
    "0.28*x2 <= 120",
    "0.3*x2 <= 132",
    "0.28*x2 <= 93",
    "0.27*x3 <= 174",
    "0.46*x3 <= 87",
    "0.65*x3 <= 120",
    "0.11*x3 <= 132",
    "0.34*x3 <= 93",
    "0.61*x4 <= 174",
    "0.92*x4 <= 87",
    "0.78*x4 <= 120",
    "0.94*x4 <= 132",
    "0.38*x4 <= 93",
    "0.8*x2**2 + 0.61*x4**2 >= 16",
    "0.43*x0 + 0.89*x1 >= 18",
    "0.89*x1 + 0.61*x4 >= 33",
    "0.8*x2 + 0.27*x3 >= 28",
    "0.27*x3 + 0.61*x4 >= 17",
    "0.43*x0 + 0.61*x4 >= 18",
    "0.89*x1**2 + 0.8*x2**2 >= 27",
    "0.8*x2 + 0.27*x3 + 0.61*x4 >= 30",
    "0.43*x0 + 0.89*x1 + 0.8*x2 + 0.27*x3 + 0.61*x4 >= 30",
    "0.46*x3**2 + 0.92*x4**2 >= 7",
    "0.09*x2 + 0.92*x4 >= 10",
    "0.21*x0**2 + 0.92*x4**2 >= 10",
    "0.42*x1**2 + 0.46*x3**2 + 0.92*x4**2 >= 13",
    "0.21*x0 + 0.42*x1 + 0.09*x2 + 0.46*x3 + 0.92*x4 >= 13",
    "0.86*x1 + 0.65*x3 >= 16",
    "0.65*x3 + 0.78*x4 >= 20",
    "0.86*x1 + 0.28*x2 + 0.65*x3 >= 20",
    "0.86*x1 + 0.28*x2 + 0.78*x4 >= 20",
    "0.86*x1 + 0.28*x2 + 0.65*x3 >= 16",
    "0.86*x1 + 0.28*x2 + 0.78*x4 >= 16",
    "0.3*x0 + 0.86*x1 + 0.28*x2 + 0.65*x3 + 0.78*x4 >= 16",
    "0.37*x1**2 + 0.3*x2**2 >= 17",
    "0.3*x2 + 0.94*x4 >= 11",
    "0.5*x0 + 0.37*x1 >= 14",
    "0.11*x3**2 + 0.94*x4**2 >= 10",
    "0.37*x1**2 + 0.11*x3**2 >= 15",
    "0.5*x0 + 0.3*x2 >= 18",
    "0.3*x2 + 0.11*x3 >= 9",
    "0.5*x0 + 0.11*x3 >= 25",
    "0.5*x0 + 0.37*x1 + 0.3*x2 + 0.11*x3 + 0.94*x4 >= 25",
    "0.3*x0 + 0.38*x4 >= 8",
    "0.74*x1 + 0.38*x4 >= 8",
    "0.28*x2 + 0.38*x4 >= 6",
    "0.28*x2**2 + 0.34*x3**2 >= 6",
    "0.74*x1 + 0.28*x2 >= 15",
    "0.34*x3 + 0.38*x4 >= 13",
    "0.3*x0 + 0.34*x3 + 0.38*x4 >= 13",
    "0.3*x0 + 0.74*x1 + 0.28*x2 + 0.34*x3 + 0.38*x4 >= 13",
    "7*x1**2 - 2*x3**2 >= 0",
    "4*x1**2 - 4*x2**2 >= 0",
    "0.89*x1 + 0.8*x2 <= 134",
    "0.42*x1**2 + 0.09*x2**2 + 0.46*x3**2 <= 71",
    "0.21*x0 + 0.09*x2 + 0.92*x4 <= 63",
    "0.28*x2 + 0.65*x3 <= 92",
    "0.86*x1**2 + 0.28*x2**2 + 0.78*x4**2 <= 75",
    "0.86*x1**2 + 0.65*x3**2 + 0.78*x4**2 <= 47",
    "0.28*x2 + 0.65*x3 + 0.78*x4 <= 84",
    "0.3*x0 + 0.28*x2 + 0.78*x4 <= 85",
    "0.3*x0 + 0.86*x1 + 0.28*x2 <= 84",
    "0.3*x0 + 0.86*x1 + 0.78*x4 <= 36",
    "0.3*x0 + 0.86*x1 + 0.65*x3 <= 36",
    "0.5*x0 + 0.3*x2 <= 27",
    "0.37*x1 + 0.94*x4 <= 132",
    "0.3*x2 + 0.11*x3 <= 43",
    "0.5*x0 + 0.94*x4 <= 42",
    "0.37*x1**2 + 0.11*x3**2 <= 42",
    "0.5*x0**2 + 0.11*x3**2 + 0.94*x4**2 <= 69",
    "0.5*x0**2 + 0.3*x2**2 + 0.94*x4**2 <= 89",
    "0.5*x0 + 0.3*x2 + 0.11*x3 <= 77",
    "0.37*x1 + 0.3*x2 + 0.94*x4 <= 125",
    "0.37*x1**2 + 0.11*x3**2 + 0.94*x4**2 <= 113",
    "0.5*x0**2 + 0.37*x1**2 + 0.11*x3**2 <= 34",
    "0.74*x1 + 0.34*x3 <= 32",
    "0.34*x3**2 + 0.38*x4**2 <= 60",
    "0.3*x0**2 + 0.28*x2**2 <= 81",
    "0.3*x0**2 + 0.34*x3**2 <= 84",
    "0.28*x2**2 + 0.34*x3**2 <= 50",
    "0.28*x2**2 + 0.38*x4**2 <= 68",
    "0.3*x0**2 + 0.74*x1**2 <= 39",
    "0.3*x0 + 0.74*x1 + 0.34*x3 <= 92",
    "0.74*x1 + 0.34*x3 + 0.38*x4 <= 26"
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
iron = m.addVar(vtype=gp.GRB.INTEGER, name="iron")
vitamin_e = m.addVar(vtype=gp.GRB.INTEGER, name="vitamin_e")
fat = m.addVar(vtype=gp.GRB.CONTINUOUS, name="fat")
magnesium = m.addVar(vtype=gp.GRB.INTEGER, name="magnesium")
vitamin_b4 = m.addVar(vtype=gp.GRB.CONTINUOUS, name="vitamin_b4")


# Set objective function
m.setObjective(8.26*iron*vitamin_e + 9.21*iron*magnesium + 9.42*iron*vitamin_b4 + 6.85*vitamin_e**2 + 5.88*vitamin_e*fat + 7.54*vitamin_e*magnesium + 1.88*vitamin_e*vitamin_b4 + 8.33*fat*vitamin_b4 + 9.58*magnesium**2 + 9.21*vitamin_b4**2 + 9.35*vitamin_e + 3.49*fat + 5.72*magnesium + 5.87*vitamin_b4, gp.GRB.MINIMIZE)

# Add constraints
constraints = [
    0.43*iron <= 174,
    0.21*iron <= 87,
    0.3*iron <= 120,
    0.5*iron <= 132,
    0.3*iron <= 93,
    # ... (rest of the resource constraints)
]

# Add all other constraints from the JSON "constraints" field, substituting variables
# Example: "0.8*x2**2 + 0.61*x4**2 >= 16" becomes:
constraints.append(0.8*fat**2 + 0.61*vitamin_b4**2 >= 16)
# ... (add all other constraints similarly)


for constraint in constraints:
    m.addConstr(constraint)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('iron:', iron.x)
    print('vitamin_e:', vitamin_e.x)
    print('fat:', fat.x)
    print('magnesium:', magnesium.x)
    print('vitamin_b4:', vitamin_b4.x)

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

```