## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B6', 'milligrams of potassium', 'grams of carbohydrates', 'milligrams of vitamin B4']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B6
- $x_1$ : milligrams of potassium
- $x_2$ : grams of carbohydrates
- $x_3$ : milligrams of vitamin B4

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $8.98x_0 + 1.03x_1 + 7.98x_2 + 3.6x_3$

## 4: List the constraints in symbolic notation
Constraints:
1. $2x_0 + 5x_1 + 5x_2 + 2x_3 \leq 77$
2. $8x_0 + 5x_1 + 5x_2 + 3x_3 \leq 64$
3. $8x_0 + 3x_1 + x_2 + 5x_3 \leq 80$
4. $5x_1 + 2x_3 \geq 12$
5. $5x_2 + 2x_3 \geq 13$
6. $2x_0 + 5x_1 + 2x_3 \geq 10$
7. $2x_0 + 5x_2 + 2x_3 \geq 10$
8. $2x_0 + 5x_1 + 2x_3 \geq 15$
9. $2x_0 + 5x_2 + 2x_3 \geq 15$
10. $8x_0 + 5x_1 + 5x_2 \geq 14$
11. $8x_0 + 3x_1 + x_2 \geq 16$
12. $8x_0 + x_2 + 5x_3 \geq 16$
13. $8x_0 + 3x_1 + x_2 \geq 12$
14. $8x_0 + x_2 + 5x_3 \geq 12$
15. $5x_1 + 2x_3 \leq 52$
16. $2x_0 + 5x_1 + 5x_2 + 2x_3 \leq 52$
17. $8x_0 + 5x_1 \leq 37$
18. $8x_0 + 5x_2 \leq 55$
19. $5x_1 + 5x_2 \leq 52$
20. $5x_1 + 3x_3 \leq 53$
21. $5x_1 + 5x_2 + 3x_3 \leq 28$
22. $8x_0 + 5x_1 + 5x_2 + 3x_3 \leq 28$
23. $3x_1 + x_2 \leq 64$
24. $x_2 + 5x_3 \leq 28$
25. $8x_0 + x_2 \leq 56$
26. $3x_1 + 5x_3 \leq 74$
27. $8x_0 + 3x_1 \leq 53$
28. $8x_0 + 3x_1 + 5x_3 \leq 50$
29. $8x_0 + 3x_1 + 5x_2 + 5x_3 \leq 50$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B6'),
        ('x1', 'milligrams of potassium'),
        ('x2', 'grams of carbohydrates'),
        ('x3', 'milligrams of vitamin B4')
    ],
    'objective_function': '8.98*x0 + 1.03*x1 + 7.98*x2 + 3.6*x3',
    'constraints': [
        '2*x0 + 5*x1 + 5*x2 + 2*x3 <= 77',
        '8*x0 + 5*x1 + 5*x2 + 3*x3 <= 64',
        '8*x0 + 3*x1 + x2 + 5*x3 <= 80',
        '5*x1 + 2*x3 >= 12',
        '5*x2 + 2*x3 >= 13',
        '2*x0 + 5*x1 + 2*x3 >= 10',
        '2*x0 + 5*x2 + 2*x3 >= 10',
        '2*x0 + 5*x1 + 2*x3 >= 15',
        '2*x0 + 5*x2 + 2*x3 >= 15',
        '8*x0 + 5*x1 + 5*x2 >= 14',
        '8*x0 + 3*x1 + x2 >= 16',
        '8*x0 + x2 + 5*x3 >= 16',
        '8*x0 + 3*x1 + x2 >= 12',
        '8*x0 + x2 + 5*x3 >= 12',
        '5*x1 + 2*x3 <= 52',
        '2*x0 + 5*x1 + 5*x2 + 2*x3 <= 52',
        '8*x0 + 5*x1 <= 37',
        '8*x0 + 5*x2 <= 55',
        '5*x1 + 5*x2 <= 52',
        '5*x1 + 3*x3 <= 53',
        '5*x1 + 5*x2 + 3*x3 <= 28',
        '8*x0 + 5*x1 + 5*x2 + 3*x3 <= 28',
        '3*x1 + x2 <= 64',
        'x2 + 5*x3 <= 28',
        '8*x0 + x2 <= 56',
        '3*x1 + 5*x3 <= 74',
        '8*x0 + 3*x1 <= 53',
        '8*x0 + 3*x1 + 5*x3 <= 50',
        '8*x0 + 3*x1 + 5*x2 + 5*x3 <= 50'
    ]
}
```

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

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, name='x0')  # milligrams of vitamin B6
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name='x1')  # milligrams of potassium
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name='x2')  # grams of carbohydrates
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name='x3')  # milligrams of vitamin B4

    # Objective function
    model.setObjective(8.98 * x0 + 1.03 * x1 + 7.98 * x2 + 3.6 * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2 * x0 + 5 * x1 + 5 * x2 + 2 * x3 <= 77)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 + 3 * x3 <= 64)
    model.addConstr(8 * x0 + 3 * x1 + x2 + 5 * x3 <= 80)
    model.addConstr(5 * x1 + 2 * x3 >= 12)
    model.addConstr(5 * x2 + 2 * x3 >= 13)
    model.addConstr(2 * x0 + 5 * x1 + 2 * x3 >= 10)
    model.addConstr(2 * x0 + 5 * x2 + 2 * x3 >= 10)
    model.addConstr(2 * x0 + 5 * x1 + 2 * x3 >= 15)
    model.addConstr(2 * x0 + 5 * x2 + 2 * x3 >= 15)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 >= 14)
    model.addConstr(8 * x0 + 3 * x1 + x2 >= 16)
    model.addConstr(8 * x0 + x2 + 5 * x3 >= 16)
    model.addConstr(8 * x0 + 3 * x1 + x2 >= 12)
    model.addConstr(8 * x0 + x2 + 5 * x3 >= 12)
    model.addConstr(5 * x1 + 2 * x3 <= 52)
    model.addConstr(2 * x0 + 5 * x1 + 5 * x2 + 2 * x3 <= 52)
    model.addConstr(8 * x0 + 5 * x1 <= 37)
    model.addConstr(8 * x0 + 5 * x2 <= 55)
    model.addConstr(5 * x1 + 5 * x2 <= 52)
    model.addConstr(5 * x1 + 3 * x3 <= 53)
    model.addConstr(5 * x1 + 5 * x2 + 3 * x3 <= 28)
    model.addConstr(8 * x0 + 5 * x1 + 5 * x2 + 3 * x3 <= 28)
    model.addConstr(3 * x1 + x2 <= 64)
    model.addConstr(x2 + 5 * x3 <= 28)
    model.addConstr(8 * x0 + x2 <= 56)
    model.addConstr(3 * x1 + 5 * x3 <= 74)
    model.addConstr(8 * x0 + 3 * x1 <= 53)
    model.addConstr(8 * x0 + 3 * x1 + 5 * x3 <= 50)
    model.addConstr(8 * x0 + 3 * x1 + 5 * x2 + 5 * x3 <= 50)

    # 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('No solution found')

optimize_problem()
```