## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents milligrams of calcium
- $x_1$ represents milligrams of vitamin B4
- $x_2$ represents milligrams of iron
- $x_3$ represents milligrams of vitamin B7
- $x_4$ represents milligrams of vitamin B6

## Step 2: Define the symbolic representation of the problem
The objective function to maximize is: $9x_0 + 3x_1 + 5x_2 + 5x_3 + x_4$

## Step 3: List all the constraints
Constraints:
1. $x_0 + 13x_1 + 3x_2 + 8x_3 + 11x_4 \leq 471$ (muscle growth index)
2. $3x_0 + 5x_1 + 4x_2 + 10x_3 + 14x_4 \leq 163$ (energy stability index)
3. $3x_2 + 8x_3 \geq 49$
4. $13x_1 + 3x_2 + 8x_3 \geq 65$
5. $x_0 + 13x_1 + 11x_4 \geq 65$
6. $x_0 + 8x_3 + 11x_4 \geq 65$
7. $13x_1 + 3x_2 + 8x_3 \geq 54$
8. $x_0 + 13x_1 + 11x_4 \geq 54$
9. $x_0 + 8x_3 + 11x_4 \geq 54$
10. $13x_1 + 3x_2 + 8x_3 \geq 84$
11. $x_0 + 13x_1 + 11x_4 \geq 84$
12. $x_0 + 8x_3 + 11x_4 \geq 84$
13. $4x_2 + 14x_4 \geq 27$
14. $3x_0 + 10x_3 \geq 11$
15. $3x_0 + 4x_2 \geq 24$
16. $10x_3 + 14x_4 \geq 16$
17. $5x_1 + 14x_4 \geq 16$
18. $5x_2 - x_3 + 8x_4 \geq 0$
19. $x_0 + 11x_4 \leq 398$
20. $x_0 + 8x_3 \leq 262$
21. $13x_1 + 3x_2 \leq 200$
22. $3x_2 + 11x_4 \leq 469$
23. $x_0 + 3x_2 \leq 350$
24. $x_0 + 13x_1 \leq 426$
25. $8x_3 + 11x_4 \leq 227$
26. $13x_1 + 11x_4 \leq 194$
27. $13x_1 + 8x_3 \leq 266$
28. $x_0 + 13x_1 + 11x_4 \leq 187$
29. $x_0 + 13x_1 + 3x_2 + 8x_3 + 11x_4 \leq 187$
30. $4x_2 + 10x_3 \leq 111$
31. $5x_1 + 4x_2 \leq 159$
32. $5x_1 + 10x_3 \leq 84$
33. $3x_0 + 5x_1 \leq 109$
34. $5x_1 + 14x_4 \leq 157$
35. $3x_0 + 10x_3 \leq 60$
36. $3x_0 + 4x_2 \leq 42$
37. $3x_0 + 5x_1 + 4x_2 \leq 79$
38. $10x_3 + 14x_4 \leq 103$
39. $3x_0 + 5x_1 + 4x_2 + 10x_3 + 14x_4 \leq 103$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of calcium'), 
        ('x1', 'milligrams of vitamin B4'), 
        ('x2', 'milligrams of iron'), 
        ('x3', 'milligrams of vitamin B7'), 
        ('x4', 'milligrams of vitamin B6')
    ], 
    'objective_function': '9*x0 + 3*x1 + 5*x2 + 5*x3 + x4', 
    'constraints': [
        'x0 + 13*x1 + 3*x2 + 8*x3 + 11*x4 <= 471',
        '3*x0 + 5*x1 + 4*x2 + 10*x3 + 14*x4 <= 163',
        '3*x2 + 8*x3 >= 49',
        '13*x1 + 3*x2 + 8*x3 >= 65',
        'x0 + 13*x1 + 11*x4 >= 65',
        'x0 + 8*x3 + 11*x4 >= 65',
        '13*x1 + 3*x2 + 8*x3 >= 54',
        'x0 + 13*x1 + 11*x4 >= 54',
        'x0 + 8*x3 + 11*x4 >= 54',
        '13*x1 + 3*x2 + 8*x3 >= 84',
        'x0 + 13*x1 + 11*x4 >= 84',
        'x0 + 8*x3 + 11*x4 >= 84',
        '4*x2 + 14*x4 >= 27',
        '3*x0 + 10*x3 >= 11',
        '3*x0 + 4*x2 >= 24',
        '10*x3 + 14*x4 >= 16',
        '5*x1 + 14*x4 >= 16',
        '5*x2 - x3 + 8*x4 >= 0',
        'x0 + 11*x4 <= 398',
        'x0 + 8*x3 <= 262',
        '13*x1 + 3*x2 <= 200',
        '3*x2 + 11*x4 <= 469',
        'x0 + 3*x2 <= 350',
        'x0 + 13*x1 <= 426',
        '8*x3 + 11*x4 <= 227',
        '13*x1 + 11*x4 <= 194',
        '13*x1 + 8*x3 <= 266',
        'x0 + 13*x1 + 11*x4 <= 187',
        'x0 + 13*x1 + 3*x2 + 8*x3 + 11*x4 <= 187',
        '4*x2 + 10*x3 <= 111',
        '5*x1 + 4*x2 <= 159',
        '5*x1 + 10*x3 <= 84',
        '3*x0 + 5*x1 <= 109',
        '5*x1 + 14*x4 <= 157',
        '3*x0 + 10*x3 <= 60',
        '3*x0 + 4*x2 <= 42',
        '3*x0 + 5*x1 + 4*x2 <= 79',
        '10*x3 + 14*x4 <= 103',
        '3*x0 + 5*x1 + 4*x2 + 10*x3 + 14*x4 <= 103'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x0")  # milligrams of calcium
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x1")  # milligrams of vitamin B4
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x2")  # milligrams of iron
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin B7
    x4 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x4")  # milligrams of vitamin B6

    # Objective function
    model.setObjective(9 * x0 + 3 * x1 + 5 * x2 + 5 * x3 + x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x0 + 13 * x1 + 3 * x2 + 8 * x3 + 11 * x4 <= 471)
    model.addConstr(3 * x0 + 5 * x1 + 4 * x2 + 10 * x3 + 14 * x4 <= 163)
    model.addConstr(3 * x2 + 8 * x3 >= 49)
    model.addConstr(13 * x1 + 3 * x2 + 8 * x3 >= 65)
    model.addConstr(x0 + 13 * x1 + 11 * x4 >= 65)
    model.addConstr(x0 + 8 * x3 + 11 * x4 >= 65)
    model.addConstr(13 * x1 + 3 * x2 + 8 * x3 >= 54)
    model.addConstr(x0 + 13 * x1 + 11 * x4 >= 54)
    model.addConstr(x0 + 8 * x3 + 11 * x4 >= 54)
    model.addConstr(13 * x1 + 3 * x2 + 8 * x3 >= 84)
    model.addConstr(x0 + 13 * x1 + 11 * x4 >= 84)
    model.addConstr(x0 + 8 * x3 + 11 * x4 >= 84)
    model.addConstr(4 * x2 + 14 * x4 >= 27)
    model.addConstr(3 * x0 + 10 * x3 >= 11)
    model.addConstr(3 * x0 + 4 * x2 >= 24)
    model.addConstr(10 * x3 + 14 * x4 >= 16)
    model.addConstr(5 * x1 + 14 * x4 >= 16)
    model.addConstr(5 * x2 - x3 + 8 * x4 >= 0)
    model.addConstr(x0 + 11 * x4 <= 398)
    model.addConstr(x0 + 8 * x3 <= 262)
    model.addConstr(13 * x1 + 3 * x2 <= 200)
    model.addConstr(3 * x2 + 11 * x4 <= 469)
    model.addConstr(x0 + 3 * x2 <= 350)
    model.addConstr(x0 + 13 * x1 <= 426)
    model.addConstr(8 * x3 + 11 * x4 <= 227)
    model.addConstr(13 * x1 + 11 * x4 <= 194)
    model.addConstr(13 * x1 + 8 * x3 <= 266)
    model.addConstr(x0 + 13 * x1 + 11 * x4 <= 187)
    model.addConstr(x0 + 13 * x1 + 3 * x2 + 8 * x3 + 11 * x4 <= 187)
    model.addConstr(4 * x2 + 10 * x3 <= 111)
    model.addConstr(5 * x1 + 4 * x2 <= 159)
    model.addConstr(5 * x1 + 10 * x3 <= 84)
    model.addConstr(3 * x0 + 5 * x1 <= 109)
    model.addConstr(5 * x1 + 14 * x4 <= 157)
    model.addConstr(3 * x0 + 10 * x3 <= 60)
    model.addConstr(3 * x0 + 4 * x2 <= 42)
    model.addConstr(3 * x0 + 5 * x1 + 4 * x2 <= 79)
    model.addConstr(10 * x3 + 14 * x4 <= 103)
    model.addConstr(3 * x0 + 5 * x1 + 4 * x2 + 10 * x3 + 14 * x4 <= 103)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Objective: ", model.objVal)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
        print("x4: ", x4.varValue)
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```