## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$: grams of protein
- $x_1$: milligrams of zinc
- $x_2$: grams of fiber
- $x_3$: milligrams of vitamin B4

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is: $9x_0 + 5x_1 + 4x_2 + 5x_3$

## 3: List the constraints in symbolic notation
Constraints:
1. $x_0 + 9x_2 \geq 37$
2. $9x_2 + 13x_3 \geq 40$
3. $23x_1 + 9x_2 \geq 45$
4. $23x_1 + 9x_2 + 13x_3 \geq 80$
5. $14x_1 + 7x_3 \geq 39$
6. $17x_0 + 14x_1 + 21x_2 \geq 64$
7. $17x_0 + 14x_1 + 7x_3 \geq 64$
8. $17x_0 + 21x_2 + 7x_3 \geq 64$
9. $14x_1 + 21x_2 + 7x_3 \geq 64$
10. $17x_0 + 14x_1 + 21x_2 \geq 66$
11. $17x_0 + 14x_1 + 7x_3 \geq 66$
12. $17x_0 + 21x_2 + 7x_3 \geq 66$
13. $14x_1 + 21x_2 + 7x_3 \geq 66$
14. $17x_0 + 14x_1 + 21x_2 \geq 50$
15. $17x_0 + 14x_1 + 7x_3 \geq 50$
16. $17x_0 + 21x_2 + 7x_3 \geq 50$
17. $14x_1 + 21x_2 + 7x_3 \geq 50$
18. $17x_0 + 14x_1 + 21x_2 \geq 36$
19. $17x_0 + 14x_1 + 7x_3 \geq 36$
20. $17x_0 + 21x_2 + 7x_3 \geq 36$
21. $14x_1 + 21x_2 + 7x_3 \geq 36$
22. $10x_1 + 19x_3 \geq 110$
23. $15x_0 + 19x_2 \geq 59$
24. $10x_1 + 19x_2 \geq 101$
25. $15x_0 + 10x_1 \geq 81$
26. $19x_2 + 19x_3 \geq 88$
27. $5x_1 - 2x_2 + 9x_3 \geq 0$
28. $x_0 + 13x_3 \leq 355$
29. $23x_1 + 13x_3 \leq 219$
30. $x_0 + 23x_1 + 9x_2 + 13x_3 \leq 219$
31. $14x_1 + 21x_2 \leq 252$
32. $15x_0 + 19x_2 \leq 263$
33. $14x_1 + 7x_3 \leq 199$
34. $17x_0 + 10x_1 \leq 187$
35. $17x_0 + 10x_1 + 7x_3 \leq 245$
36. $10x_1 + 21x_2 + 7x_3 \leq 239$
37. $17x_0 + 14x_1 + 21x_2 \leq 117$
38. $17x_0 + 21x_2 + 7x_3 \leq 96$
39. $17x_0 + 14x_1 + 21x_2 + 7x_3 \leq 96$
40. $15x_0 + 19x_3 \leq 434$
41. $15x_0 + 10x_1 \leq 307$
42. $10x_1 + 19x_3 \leq 254$
43. $19x_2 + 19x_3 \leq 250$
44. $10x_1 + 19x_2 + 19x_3 \leq 282$
45. $15x_0 + 10x_1 + 19x_3 \leq 248$
46. $15x_0 + 10x_1 + 19x_2 \leq 234$
47. $15x_0 + 10x_1 + 19x_2 + 19x_3 \leq 234$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'grams of protein'),
        ('x1', 'milligrams of zinc'),
        ('x2', 'grams of fiber'),
        ('x3', 'milligrams of vitamin B4')
    ],
    'objective_function': '9*x0 + 5*x1 + 4*x2 + 5*x3',
    'constraints': [
        'x0 + 9*x2 >= 37',
        '9*x2 + 13*x3 >= 40',
        '23*x1 + 9*x2 >= 45',
        '23*x1 + 9*x2 + 13*x3 >= 80',
        '14*x1 + 7*x3 >= 39',
        '17*x0 + 14*x1 + 21*x2 >= 64',
        '17*x0 + 14*x1 + 7*x3 >= 64',
        '17*x0 + 21*x2 + 7*x3 >= 64',
        '14*x1 + 21*x2 + 7*x3 >= 64',
        '17*x0 + 14*x1 + 21*x2 >= 66',
        '17*x0 + 14*x1 + 7*x3 >= 66',
        '17*x0 + 21*x2 + 7*x3 >= 66',
        '14*x1 + 21*x2 + 7*x3 >= 66',
        '17*x0 + 14*x1 + 21*x2 >= 50',
        '17*x0 + 14*x1 + 7*x3 >= 50',
        '17*x0 + 21*x2 + 7*x3 >= 50',
        '14*x1 + 21*x2 + 7*x3 >= 50',
        '17*x0 + 14*x1 + 21*x2 >= 36',
        '17*x0 + 14*x1 + 7*x3 >= 36',
        '17*x0 + 21*x2 + 7*x3 >= 36',
        '14*x1 + 21*x2 + 7*x3 >= 36',
        '10*x1 + 19*x3 >= 110',
        '15*x0 + 19*x2 >= 59',
        '10*x1 + 19*x2 >= 101',
        '15*x0 + 10*x1 >= 81',
        '19*x2 + 19*x3 >= 88',
        '5*x1 - 2*x2 + 9*x3 >= 0',
        'x0 + 13*x3 <= 355',
        '23*x1 + 13*x3 <= 219',
        'x0 + 23*x1 + 9*x2 + 13*x3 <= 219',
        '14*x1 + 21*x2 <= 252',
        '15*x0 + 19*x2 <= 263',
        '14*x1 + 7*x3 <= 199',
        '17*x0 + 10*x1 <= 187',
        '17*x0 + 10*x1 + 7*x3 <= 245',
        '10*x1 + 21*x2 + 7*x3 <= 239',
        '17*x0 + 14*x1 + 21*x2 <= 117',
        '17*x0 + 21*x2 + 7*x3 <= 96',
        '17*x0 + 14*x1 + 21*x2 + 7*x3 <= 96',
        '15*x0 + 19*x3 <= 434',
        '15*x0 + 10*x1 <= 307',
        '10*x1 + 19*x3 <= 254',
        '19*x2 + 19*x3 <= 250',
        '10*x1 + 19*x2 + 19*x3 <= 282',
        '15*x0 + 10*x1 + 19*x3 <= 248',
        '15*x0 + 10*x1 + 19*x2 <= 234',
        '15*x0 + 10*x1 + 19*x2 + 19*x3 <= 234'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # grams of protein
    x1 = model.addVar(name="x1", lb=0)  # milligrams of zinc
    x2 = model.addVar(name="x2", lb=0)  # grams of fiber
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B4

    # Objective function
    model.setObjective(9 * x0 + 5 * x1 + 4 * x2 + 5 * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x0 + 9 * x2 >= 37)
    model.addConstr(9 * x2 + 13 * x3 >= 40)
    model.addConstr(23 * x1 + 9 * x2 >= 45)
    model.addConstr(23 * x1 + 9 * x2 + 13 * x3 >= 80)
    model.addConstr(14 * x1 + 7 * x3 >= 39)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 >= 64)
    model.addConstr(17 * x0 + 14 * x1 + 7 * x3 >= 64)
    model.addConstr(17 * x0 + 21 * x2 + 7 * x3 >= 64)
    model.addConstr(14 * x1 + 21 * x2 + 7 * x3 >= 64)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 >= 66)
    model.addConstr(17 * x0 + 14 * x1 + 7 * x3 >= 66)
    model.addConstr(17 * x0 + 21 * x2 + 7 * x3 >= 66)
    model.addConstr(14 * x1 + 21 * x2 + 7 * x3 >= 66)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 >= 50)
    model.addConstr(17 * x0 + 14 * x1 + 7 * x3 >= 50)
    model.addConstr(17 * x0 + 21 * x2 + 7 * x3 >= 50)
    model.addConstr(14 * x1 + 21 * x2 + 7 * x3 >= 50)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 >= 36)
    model.addConstr(17 * x0 + 14 * x1 + 7 * x3 >= 36)
    model.addConstr(17 * x0 + 21 * x2 + 7 * x3 >= 36)
    model.addConstr(14 * x1 + 21 * x2 + 7 * x3 >= 36)
    model.addConstr(10 * x1 + 19 * x3 >= 110)
    model.addConstr(15 * x0 + 19 * x2 >= 59)
    model.addConstr(10 * x1 + 19 * x2 >= 101)
    model.addConstr(15 * x0 + 10 * x1 >= 81)
    model.addConstr(19 * x2 + 19 * x3 >= 88)
    model.addConstr(5 * x1 - 2 * x2 + 9 * x3 >= 0)
    model.addConstr(x0 + 13 * x3 <= 355)
    model.addConstr(23 * x1 + 13 * x3 <= 219)
    model.addConstr(x0 + 23 * x1 + 9 * x2 + 13 * x3 <= 219)
    model.addConstr(14 * x1 + 21 * x2 <= 252)
    model.addConstr(15 * x0 + 19 * x2 <= 263)
    model.addConstr(14 * x1 + 7 * x3 <= 199)
    model.addConstr(17 * x0 + 10 * x1 <= 187)
    model.addConstr(17 * x0 + 10 * x1 + 7 * x3 <= 245)
    model.addConstr(10 * x1 + 21 * x2 + 7 * x3 <= 239)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 <= 117)
    model.addConstr(17 * x0 + 21 * x2 + 7 * x3 <= 96)
    model.addConstr(17 * x0 + 14 * x1 + 21 * x2 + 7 * x3 <= 96)
    model.addConstr(15 * x0 + 19 * x3 <= 434)
    model.addConstr(15 * x0 + 10 * x1 <= 307)
    model.addConstr(10 * x1 + 19 * x3 <= 254)
    model.addConstr(19 * x2 + 19 * x3 <= 250)
    model.addConstr(10 * x1 + 19 * x2 + 19 * x3 <= 282)
    model.addConstr(15 * x0 + 10 * x1 + 19 * x3 <= 248)
    model.addConstr(15 * x0 + 10 * x1 + 19 * x2 <= 234)
    model.addConstr(15 * x0 + 10 * x1 + 19 * x2 + 19 * x3 <= 234)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```