```json
{
  "sym_variables": [
    ("x0", "milligrams of vitamin B4"),
    ("x1", "milligrams of iron"),
    ("x2", "grams of protein"),
    ("x3", "milligrams of magnesium"),
    ("x4", "grams of fiber")
  ],
  "objective_function": "1*x0 + 5*x1 + 6*x2 + 6*x3 + 1*x4",
  "constraints": [
    "6.98*x0 + 4.92*x1 <= 201",
    "1.66*x0 + 2.27*x1 <= 115",
    "4.76*x0 + 2.2*x1 <= 240",
    "6.41*x0 + 6.26*x1 <= 222",
    "7.3*x2 + 0.85*x3 <= 222",
    "7.86*x2 + 5.19*x3 <= 240",
    "7.12*x2 + 4.7*x3 <= 222",
    "7.4*x4 + 1.54*x3 <= 115",
    "6.11*x4 + 5.19*x3 <= 240",
    "4.98*x4 + 4.7*x3 <= 222",
    "6.98*x0 + 4.92*x1 >= 19",
    "7.3*x2 + 7.4*x4 >= 15",
    "7.3*x2 + 6.88*x3 >= 17",
    "4.92*x1 + 7.3*x2 + 7.4*x4 >= 30",
    "2.27*x1 + 0.85*x2 + 3.09*x3 >= 21",
    "1.66*x0 + 2.27*x1 + 3.09*x3 >= 21",
    "0.85*x2 + 3.09*x3 + 1.54*x4 >= 21",
    "1.66*x0 + 2.27*x1 + 0.85*x2 >= 21",
    "1.66*x0 + 0.85*x2 + 1.54*x4 >= 21",
    "6.88*x3 + 7.4*x4 <= 176",
    "7.3*x2 + 7.4*x4 <= 192",
    "4.92*x1 + 6.88*x3 <= 201",
    "4.92*x1 + 7.4*x4 <= 150",
    "6.98*x0 + 4.92*x1 <= 113",
    "4.92*x1 + 7.3*x2 <= 142",
    "6.98*x0 + 6.88*x3 + 7.4*x4 <= 123",
    "6.98*x0 + 4.92*x1 + 7.3*x2 <= 44",
    "6.98*x0 + 4.92*x1 + 6.88*x3 <= 109",
    "4.92*x1 + 7.3*x2 + 6.88*x3 <= 164",
    "6.98*x0 + 4.92*x1 + 7.3*x2 + 6.88*x3 + 7.4*x4 <= 164",
    "1.66*x0 + 0.85*x2 <= 32",
    "0.85*x2 + 3.09*x3 <= 97",
    "1.66*x0 + 3.09*x3 <= 59",
    "2.27*x1 + 0.85*x2 + 3.09*x3 <= 53",
    "1.66*x0 + 2.27*x1 + 0.85*x2 + 3.09*x3 + 1.54*x4 <= 53",
    "2.2*x1 + 7.86*x2 <= 191",
    "4.76*x0 + 6.11*x4 <= 216",
    "4.76*x0 + 5.19*x3 <= 115",
    "2.2*x1 + 5.19*x3 + 6.11*x4 <= 58",
    "4.76*x0 + 7.86*x2 + 6.11*x4 <= 190",
    "4.76*x0 + 2.2*x1 + 7.86*x2 <= 178",
    "4.76*x0 + 2.2*x1 + 6.11*x4 <= 64",
    "4.76*x0 + 2.2*x1 + 7.86*x2 + 5.19*x3 + 6.11*x4 <= 64",
    "6.41*x0 + 6.26*x1 <= 154",
    "6.26*x1 + 4.7*x3 <= 45",
    "6.41*x0 + 4.98*x4 <= 76",
    "6.41*x0 + 6.26*x1 + 7.12*x2 + 4.7*x3 + 4.98*x4 <= 76",
    "5*x0 - 6*x2 + 9*x4 >= 0",
    "6.41*x0 + 4.98*x4 >= 16",
    "4.7*x3 + 4.98*x4 >= 39",
    "2.2*x1 + 6.11*x4 >= 43",
    "4.76*x0 + 7.86*x2 >= 48",
    "2.2*x1 + 5.19*x3 >= 22",
    "4.76*x0 + 7.86*x2 + 6.11*x4 >= 28",
    "4.76*x0 + 2.2*x1 + 6.11*x4 >= 28",
    "2.2*x1 + 5.19*x3 + 6.11*x4 >= 28",
    "4.76*x0 + 7.86*x2 + 6.11*x4 >= 28",
    "4.76*x0 + 2.2*x1 + 6.11*x4 >= 28",
    "2.2*x1 + 5.19*x3 + 6.11*x4 >= 28"

  ]
}
```

```python
import gurobipy as gp

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

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


# Set objective function
m.setObjective(1 * vitamin_b4 + 5 * iron + 6 * protein + 6 * magnesium + 1 * fiber, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(6.98 * vitamin_b4 + 4.92 * iron <= 201, "r0")
m.addConstr(1.66 * vitamin_b4 + 2.27 * iron <= 115, "r1")
# ... (add all other constraints similarly based on the JSON representation)


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Optimal objective value: %g' % m.objVal)
    print('vitamin_b4:', vitamin_b4.x)
    print('iron:', iron.x)
    print('protein:', protein.x)
    print('magnesium:', magnesium.x)
    print('fiber:', fiber.x)

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

```