## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_0$ represents milligrams of vitamin B6
- $x_1$ represents milligrams of vitamin B7
- $x_2$ represents milligrams of magnesium
- $x_3$ represents milligrams of calcium

## Step 2: Translate the objective function into algebraic terms
The objective function to maximize is $1.28x_0 + 6.28x_1 + 6.55x_2 + 5.43x_3$.

## 3: List the constraints in algebraic terms
Constraints from the problem description:
1. $4x_1 + x_3 \geq 44$
2. $28x_0 + 35x_2 \geq 76$
3. $4x_1 + 35x_2 + 31x_3 \geq 54$
4. $28x_0 + 4x_1 + 35x_2 \geq 54$
5. $28x_0 + 4x_1 + 31x_3 \geq 54$
6. $28x_0 + 35x_2 + 31x_3 \geq 54$
7. $4x_1 + 35x_2 + 31x_3 \geq 70$
8. $28x_0 + 4x_1 + 35x_2 \geq 70$
9. $28x_0 + 4x_1 + 31x_3 \geq 70$
10. $28x_0 + 35x_2 + 31x_3 \geq 70$
11. $4x_1 + 35x_2 + 31x_3 \geq 67$
12. $28x_0 + 4x_1 + 35x_2 \geq 67$
13. $28x_0 + 4x_1 + 31x_3 \geq 67$
14. $28x_0 + 35x_2 + 31x_3 \geq 67$
15. $11x_2 + 16x_3 \geq 54$
16. $4x_0 + 16x_3 \geq 42$
17. $4x_0 + 12x_1 \geq 70$
18. $4x_0 + 11x_2 \geq 95$
19. $4x_0 + 11x_2 + 16x_3 \geq 80$
20. $4x_0 + 12x_1 + 11x_2 \geq 80$
21. $4x_0 + 12x_1 + 16x_3 \geq 80$
22. $4x_0 + 11x_2 + 16x_3 \geq 85$
23. $4x_0 + 12x_1 + 11x_2 \geq 85$
24. $4x_0 + 12x_1 + 16x_3 \geq 85$
25. $4x_0 + 11x_2 + 16x_3 \geq 58$
26. $4x_0 + 12x_1 + 11x_2 \geq 58$
27. $4x_0 + 12x_1 + 16x_3 \geq 58$
28. $-9x_0 + 2x_3 \geq 0$
29. $4x_1 + 31x_3 \leq 313$
30. $4x_1 + 35x_2 \leq 113$
31. $28x_0 + 4x_1 + 31x_3 \leq 180$
32. $28x_0 + 4x_1 + 35x_2 + 31x_3 \leq 180$
33. $12x_1 + 16x_3 \leq 226$
34. $4x_0 + 11x_2 \leq 302$
35. $11x_2 + 16x_3 \leq 371$
36. $4x_0 + 12x_1 + 11x_2 + 16x_3 \leq 371$

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B6'),
        ('x1', 'milligrams of vitamin B7'),
        ('x2', 'milligrams of magnesium'),
        ('x3', 'milligrams of calcium')
    ],
    'objective_function': '1.28*x0 + 6.28*x1 + 6.55*x2 + 5.43*x3',
    'constraints': [
        '4*x1 + x3 >= 44',
        '28*x0 + 35*x2 >= 76',
        '4*x1 + 35*x2 + 31*x3 >= 54',
        '28*x0 + 4*x1 + 35*x2 >= 54',
        '28*x0 + 4*x1 + 31*x3 >= 54',
        '28*x0 + 35*x2 + 31*x3 >= 54',
        '4*x1 + 35*x2 + 31*x3 >= 70',
        '28*x0 + 4*x1 + 35*x2 >= 70',
        '28*x0 + 4*x1 + 31*x3 >= 70',
        '28*x0 + 35*x2 + 31*x3 >= 70',
        '4*x1 + 35*x2 + 31*x3 >= 67',
        '28*x0 + 4*x1 + 35*x2 >= 67',
        '28*x0 + 4*x1 + 31*x3 >= 67',
        '28*x0 + 35*x2 + 31*x3 >= 67',
        '11*x2 + 16*x3 >= 54',
        '4*x0 + 16*x3 >= 42',
        '4*x0 + 12*x1 >= 70',
        '4*x0 + 11*x2 >= 95',
        '4*x0 + 11*x2 + 16*x3 >= 80',
        '4*x0 + 12*x1 + 11*x2 >= 80',
        '4*x0 + 12*x1 + 16*x3 >= 80',
        '4*x0 + 11*x2 + 16*x3 >= 85',
        '4*x0 + 12*x1 + 11*x2 >= 85',
        '4*x0 + 12*x1 + 16*x3 >= 85',
        '4*x0 + 11*x2 + 16*x3 >= 58',
        '4*x0 + 12*x1 + 11*x2 >= 58',
        '4*x0 + 12*x1 + 16*x3 >= 58',
        '-9*x0 + 2*x3 >= 0',
        '4*x1 + 31*x3 <= 313',
        '4*x1 + 35*x2 <= 113',
        '28*x0 + 4*x1 + 31*x3 <= 180',
        '28*x0 + 4*x1 + 35*x2 + 31*x3 <= 180',
        '12*x1 + 16*x3 <= 226',
        '4*x0 + 11*x2 <= 302',
        '11*x2 + 16*x3 <= 371',
        '4*x0 + 12*x1 + 11*x2 + 16*x3 <= 371'
    ]
}
```

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

def solve_optimization_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 vitamin B7
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x2")  # milligrams of magnesium
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x3")  # milligrams of calcium

    # Objective function
    model.setObjective(1.28*x0 + 6.28*x1 + 6.55*x2 + 5.43*x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(4*x1 + x3 >= 44)
    model.addConstr(28*x0 + 35*x2 >= 76)
    model.addConstr(4*x1 + 35*x2 + 31*x3 >= 54)
    model.addConstr(28*x0 + 4*x1 + 35*x2 >= 54)
    model.addConstr(28*x0 + 4*x1 + 31*x3 >= 54)
    model.addConstr(28*x0 + 35*x2 + 31*x3 >= 54)
    model.addConstr(4*x1 + 35*x2 + 31*x3 >= 70)
    model.addConstr(28*x0 + 4*x1 + 35*x2 >= 70)
    model.addConstr(28*x0 + 4*x1 + 31*x3 >= 70)
    model.addConstr(28*x0 + 35*x2 + 31*x3 >= 70)
    model.addConstr(4*x1 + 35*x2 + 31*x3 >= 67)
    model.addConstr(28*x0 + 4*x1 + 35*x2 >= 67)
    model.addConstr(28*x0 + 4*x1 + 31*x3 >= 67)
    model.addConstr(28*x0 + 35*x2 + 31*x3 >= 67)
    model.addConstr(11*x2 + 16*x3 >= 54)
    model.addConstr(4*x0 + 16*x3 >= 42)
    model.addConstr(4*x0 + 12*x1 >= 70)
    model.addConstr(4*x0 + 11*x2 >= 95)
    model.addConstr(4*x0 + 11*x2 + 16*x3 >= 80)
    model.addConstr(4*x0 + 12*x1 + 11*x2 >= 80)
    model.addConstr(4*x0 + 12*x1 + 16*x3 >= 80)
    model.addConstr(4*x0 + 11*x2 + 16*x3 >= 85)
    model.addConstr(4*x0 + 12*x1 + 11*x2 >= 85)
    model.addConstr(4*x0 + 12*x1 + 16*x3 >= 85)
    model.addConstr(4*x0 + 11*x2 + 16*x3 >= 58)
    model.addConstr(4*x0 + 12*x1 + 11*x2 >= 58)
    model.addConstr(4*x0 + 12*x1 + 16*x3 >= 58)
    model.addConstr(-9*x0 + 2*x3 >= 0)
    model.addConstr(4*x1 + 31*x3 <= 313)
    model.addConstr(4*x1 + 35*x2 <= 113)
    model.addConstr(28*x0 + 4*x1 + 31*x3 <= 180)
    model.addConstr(28*x0 + 4*x1 + 35*x2 + 31*x3 <= 180)
    model.addConstr(12*x1 + 16*x3 <= 226)
    model.addConstr(4*x0 + 11*x2 <= 302)
    model.addConstr(11*x2 + 16*x3 <= 371)
    model.addConstr(4*x0 + 12*x1 + 11*x2 + 16*x3 <= 371)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B6: {x0.varValue}")
        print(f"Milligrams of vitamin B7: {x1.varValue}")
        print(f"Milligrams of magnesium: {x2.varValue}")
        print(f"Milligrams of calcium: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```