## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B6', 'grams of protein', 'milligrams of vitamin B5', 'milligrams of zinc']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : milligrams of vitamin B6
- $x_2$ : grams of protein
- $x_3$ : milligrams of vitamin B5
- $x_4$ : milligrams of zinc

## 3: Define the objective function in symbolic notation
The objective function to maximize is $9x_1 + 3x_2 + 9x_3 + 2x_4$.

## 4: List the semi-algebraic constraints
Constraints:
1. $4x_2 + 10x_3 \geq 6$
2. $11x_1 + 4x_2 \geq 9$
3. $11x_1 + 4x_4 \geq 13$
4. $10x_3 + 4x_4 \geq 13$
5. $9x_1 + 11x_2 \geq 25$
6. $9x_1 + 11x_2 + 4x_4 \geq 27$
7. $6x_1 + 10x_3 \geq 9$
8. $9x_2 + 10x_3 \geq 7$
9. $11x_1 + 9x_2 + 2x_3 \geq 13$
10. $11x_1 + 4x_2 \leq 18$
11. $11x_1 + 10x_3 \leq 17$
12. $4x_2 + 10x_3 + 4x_4 \leq 26$
13. $11x_1 + 4x_2 + 10x_3 \leq 56$
14. $11x_1 + 4x_2 + 4x_4 \leq 31$
15. $11x_1 + 4x_2 + 10x_3 + 4x_4 \leq 31$
16. $9x_1 + 11x_2 \leq 91$
17. $9x_1 + 4x_4 \leq 65$
18. $8x_3 + 4x_4 \leq 44$
19. $11x_2 + 4x_4 \leq 114$
20. $11x_2 + 8x_3 \leq 114$
21. $9x_1 + 11x_2 + 8x_3 + 4x_4 \leq 114$
22. $9x_2 + 11x_4 \leq 43$
23. $6x_1 + 10x_3 \leq 23$
24. $9x_2 + 10x_3 \leq 36$
25. $6x_1 + 11x_4 \leq 41$
26. $10x_3 + 11x_4 \leq 44$
27. $6x_1 + 9x_2 + 10x_3 \leq 50$
28. $6x_1 + 10x_3 + 11x_4 \leq 67$
29. $6x_1 + 9x_2 + 10x_3 + 11x_4 \leq 67$
30. $11x_1 + 9x_2 \leq 41$
31. $9x_2 + 2x_3 \leq 24$
32. $11x_1 + 4x_4 \leq 40$
33. $11x_1 + 9x_2 + 2x_3 + 4x_4 \leq 40$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B6'),
        ('x2', 'grams of protein'),
        ('x3', 'milligrams of vitamin B5'),
        ('x4', 'milligrams of zinc')
    ],
    'objective_function': '9*x1 + 3*x2 + 9*x3 + 2*x4',
    'constraints': [
        '4*x2 + 10*x3 >= 6',
        '11*x1 + 4*x2 >= 9',
        '11*x1 + 4*x4 >= 13',
        '10*x3 + 4*x4 >= 13',
        '9*x1 + 11*x2 >= 25',
        '9*x1 + 11*x2 + 4*x4 >= 27',
        '6*x1 + 10*x3 >= 9',
        '9*x2 + 10*x3 >= 7',
        '11*x1 + 9*x2 + 2*x3 >= 13',
        '11*x1 + 4*x2 <= 18',
        '11*x1 + 10*x3 <= 17',
        '4*x2 + 10*x3 + 4*x4 <= 26',
        '11*x1 + 4*x2 + 10*x3 <= 56',
        '11*x1 + 4*x2 + 4*x4 <= 31',
        '11*x1 + 4*x2 + 10*x3 + 4*x4 <= 31',
        '9*x1 + 11*x2 <= 91',
        '9*x1 + 4*x4 <= 65',
        '8*x3 + 4*x4 <= 44',
        '11*x2 + 4*x4 <= 114',
        '11*x2 + 8*x3 <= 114',
        '9*x1 + 11*x2 + 8*x3 + 4*x4 <= 114',
        '9*x2 + 11*x4 <= 43',
        '6*x1 + 10*x3 <= 23',
        '9*x2 + 10*x3 <= 36',
        '6*x1 + 11*x4 <= 41',
        '10*x3 + 11*x4 <= 44',
        '6*x1 + 9*x2 + 10*x3 <= 50',
        '6*x1 + 10*x3 + 11*x4 <= 67',
        '6*x1 + 9*x2 + 10*x3 + 11*x4 <= 67',
        '11*x1 + 9*x2 <= 41',
        '9*x2 + 2*x3 <= 24',
        '11*x1 + 4*x4 <= 40',
        '11*x1 + 9*x2 + 2*x3 + 4*x4 <= 40'
    ]
}
```

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

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

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B6
    x2 = model.addVar(name="x2", lb=0)  # grams of protein
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B5
    x4 = model.addVar(name="x4", lb=0)  # milligrams of zinc

    # Objective function
    model.setObjective(9 * x1 + 3 * x2 + 9 * x3 + 2 * x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4 * x2 + 10 * x3 >= 6)
    model.addConstr(11 * x1 + 4 * x2 >= 9)
    model.addConstr(11 * x1 + 4 * x4 >= 13)
    model.addConstr(10 * x3 + 4 * x4 >= 13)
    model.addConstr(9 * x1 + 11 * x2 >= 25)
    model.addConstr(9 * x1 + 11 * x2 + 4 * x4 >= 27)
    model.addConstr(6 * x1 + 10 * x3 >= 9)
    model.addConstr(9 * x2 + 10 * x3 >= 7)
    model.addConstr(11 * x1 + 9 * x2 + 2 * x3 >= 13)
    model.addConstr(11 * x1 + 4 * x2 <= 18)
    model.addConstr(11 * x1 + 10 * x3 <= 17)
    model.addConstr(4 * x2 + 10 * x3 + 4 * x4 <= 26)
    model.addConstr(11 * x1 + 4 * x2 + 10 * x3 <= 56)
    model.addConstr(11 * x1 + 4 * x2 + 4 * x4 <= 31)
    model.addConstr(11 * x1 + 4 * x2 + 10 * x3 + 4 * x4 <= 31)
    model.addConstr(9 * x1 + 11 * x2 <= 91)
    model.addConstr(9 * x1 + 4 * x4 <= 65)
    model.addConstr(8 * x3 + 4 * x4 <= 44)
    model.addConstr(11 * x2 + 4 * x4 <= 114)
    model.addConstr(11 * x2 + 8 * x3 <= 114)
    model.addConstr(9 * x1 + 11 * x2 + 8 * x3 + 4 * x4 <= 114)
    model.addConstr(9 * x2 + 11 * x4 <= 43)
    model.addConstr(6 * x1 + 10 * x3 <= 23)
    model.addConstr(9 * x2 + 10 * x3 <= 36)
    model.addConstr(6 * x1 + 11 * x4 <= 41)
    model.addConstr(10 * x3 + 11 * x4 <= 44)
    model.addConstr(6 * x1 + 9 * x2 + 10 * x3 <= 50)
    model.addConstr(6 * x1 + 10 * x3 + 11 * x4 <= 67)
    model.addConstr(6 * x1 + 9 * x2 + 10 * x3 + 11 * x4 <= 67)
    model.addConstr(11 * x1 + 9 * x2 <= 41)
    model.addConstr(9 * x2 + 2 * x3 <= 24)
    model.addConstr(11 * x1 + 4 * x4 <= 40)
    model.addConstr(11 * x1 + 9 * x2 + 2 * x3 + 4 * x4 <= 40)

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B6: {x1.varValue}")
        print(f"Grams of protein: {x2.varValue}")
        print(f"Milligrams of vitamin B5: {x3.varValue}")
        print(f"Milligrams of zinc: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```