## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and their corresponding natural language objects:
- $x_0$ = 'grams of protein'
- $x_1$ = 'milligrams of vitamin B12'
- $x_2$ = 'grams of carbohydrates'

## Step 2: Formulate the objective function
The objective function to minimize is $5.44x_0 + 7.36x_1 + 9.87x_2$.

## Step 3: List all the constraints
1. $12x_0 \leq 294$
2. $7x_0 \leq 248$
3. $7x_0 \leq 115$
4. $12x_1 \leq 294$
5. $16x_1 \leq 248$
6. $23x_1 \leq 115$
7. $24x_2 \leq 294$
8. $8x_2 \leq 248$
9. $21x_2 \leq 115$
10. $12x_0 + 12x_1 \geq 64$
11. $12x_0 + 12x_1 + 24x_2 \geq 64$
12. $7x_0 + 8x_2 \geq 57$
13. $16x_1 + 8x_2 \geq 57$
14. $7x_0 + 16x_1 + 8x_2 \geq 57$
15. $23x_1 + 21x_2 \geq 27$
16. $7x_0 + 23x_1 \geq 20$
17. $7x_0 + 21x_2 \geq 14$
18. $7x_0 + 23x_1 + 21x_2 \geq 38$
19. $9x_1 - 6x_2 \geq 0$
20. $12x_0 + 12x_1 \leq 176$
21. $12x_1 + 24x_2 \leq 256$
22. $12x_0 + 24x_2 \leq 149$
23. $7x_0 + 23x_1 \leq 75$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'grams of protein'),
        ('x1', 'milligrams of vitamin B12'),
        ('x2', 'grams of carbohydrates')
    ],
    'objective_function': '5.44*x0 + 7.36*x1 + 9.87*x2',
    'constraints': [
        '12*x0 <= 294',
        '7*x0 <= 248',
        '7*x0 <= 115',
        '12*x1 <= 294',
        '16*x1 <= 248',
        '23*x1 <= 115',
        '24*x2 <= 294',
        '8*x2 <= 248',
        '21*x2 <= 115',
        '12*x0 + 12*x1 >= 64',
        '12*x0 + 12*x1 + 24*x2 >= 64',
        '7*x0 + 8*x2 >= 57',
        '16*x1 + 8*x2 >= 57',
        '7*x0 + 16*x1 + 8*x2 >= 57',
        '23*x1 + 21*x2 >= 27',
        '7*x0 + 23*x1 >= 20',
        '7*x0 + 21*x2 >= 14',
        '7*x0 + 23*x1 + 21*x2 >= 38',
        '9*x1 - 6*x2 >= 0',
        '12*x0 + 12*x1 <= 176',
        '12*x1 + 24*x2 <= 256',
        '12*x0 + 24*x2 <= 149',
        '7*x0 + 23*x1 <= 75'
    ]
}
```

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

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # grams of protein
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B12
    x2 = model.addVar(name="x2", lb=0)  # grams of carbohydrates

    # Objective function
    model.setObjective(5.44 * x0 + 7.36 * x1 + 9.87 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(12 * x0 <= 294)
    model.addConstr(7 * x0 <= 248)
    model.addConstr(7 * x0 <= 115)
    model.addConstr(12 * x1 <= 294)
    model.addConstr(16 * x1 <= 248)
    model.addConstr(23 * x1 <= 115)
    model.addConstr(24 * x2 <= 294)
    model.addConstr(8 * x2 <= 248)
    model.addConstr(21 * x2 <= 115)
    model.addConstr(12 * x0 + 12 * x1 >= 64)
    model.addConstr(12 * x0 + 12 * x1 + 24 * x2 >= 64)
    model.addConstr(7 * x0 + 8 * x2 >= 57)
    model.addConstr(16 * x1 + 8 * x2 >= 57)
    model.addConstr(7 * x0 + 16 * x1 + 8 * x2 >= 57)
    model.addConstr(23 * x1 + 21 * x2 >= 27)
    model.addConstr(7 * x0 + 23 * x1 >= 20)
    model.addConstr(7 * x0 + 21 * x2 >= 14)
    model.addConstr(7 * x0 + 23 * x1 + 21 * x2 >= 38)
    model.addConstr(9 * x1 - 6 * x2 >= 0)
    model.addConstr(12 * x0 + 12 * x1 <= 176)
    model.addConstr(12 * x1 + 24 * x2 <= 256)
    model.addConstr(12 * x0 + 24 * x2 <= 149)
    model.addConstr(7 * x0 + 23 * x1 <= 75)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objval}")
        print(f"Grams of protein: {x0.x}")
        print(f"Milligrams of vitamin B12: {x1.x}")
        print(f"Grams of carbohydrates: {x2.x}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```