## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$: milligrams of vitamin B12
- $x_1$: milligrams of magnesium
- $x_2$: milligrams of vitamin E
- $x_3$: milligrams of vitamin B5

The objective function to maximize is: $2x_0 + 6x_1 + 4x_2 + 4x_3$

## Step 2: List all constraints in symbolic notation
Constraints:
1. $6x_0 \leq 110$
2. $x_0 \leq 110$
3. $8x_0 \leq 88$
4. $4x_0 \leq 103$
5. $3x_1 \leq 110$
6. $8x_1 \leq 110$
7. $4x_1 \leq 88$
8. $5x_1 \leq 103$
9. $8x_2 \leq 110$
10. $3x_2 \leq 110$
11. $8x_2 \leq 88$
12. $10x_2 \leq 103$
13. $x_3 \leq 110$
14. $6x_3 \leq 110$
15. $9x_3 \leq 88$
16. $4x_3 \leq 103$
17. $3x_1 + 8x_2 + x_3 \geq 25$
18. $3x_2 + 6x_3 \geq 10$
19. $4x_0 + 5x_1 \geq 11$
20. $4x_0 + 10x_2 \geq 22$
21. $10x_2 + 4x_3 \geq 25$
22. $4x_0 + 4x_3 \geq 9$
23. $4x_0 + 10x_2 + 4x_3 \geq 22$
24. $2x_0 - 6x_2 \geq 0$
25. $3x_1 + 8x_2 \leq 104$
26. $3x_1 + 8x_2 + x_3 \leq 107$
27. $6x_0 + 8x_2 + x_3 \leq 50$
28. $6x_0 + 3x_1 + x_3 \leq 104$
29. $6x_0 + 3x_1 + 8x_2 \leq 71$
30. $6x_0 + 3x_1 + 8x_2 + x_3 \leq 71$
31. $x_0 + 8x_1 \leq 104$
32. $x_0 + 6x_3 \leq 80$
33. $8x_1 + 6x_3 \leq 35$
34. $x_0 + 3x_2 \leq 86$
35. $8x_1 + 3x_2 \leq 57$
36. $x_0 + 8x_1 + 6x_3 \leq 51$
37. $x_0 + 8x_1 + 3x_2 + 6x_3 \leq 51$
38. $8x_0 + 9x_3 \leq 69$
39. $4x_1 + 8x_2 \leq 67$
40. $8x_2 + 9x_3 \leq 35$
41. $8x_0 + 4x_1 \leq 60$
42. $8x_0 + 4x_1 + 8x_2 \leq 82$
43. $8x_0 + 4x_1 + 8x_2 + 9x_3 \leq 82$
44. $4x_0 + 4x_3 \leq 39$
45. $4x_0 + 5x_1 + 10x_2 + 4x_3 \leq 39$

## Step 3: Write down the symbolic representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B12'),
        ('x1', 'milligrams of magnesium'),
        ('x2', 'milligrams of vitamin E'),
        ('x3', 'milligrams of vitamin B5')
    ],
    'objective_function': '2*x0 + 6*x1 + 4*x2 + 4*x3',
    'constraints': [
        '6*x0 <= 110',
        'x0 <= 110',
        '8*x0 <= 88',
        '4*x0 <= 103',
        '3*x1 <= 110',
        '8*x1 <= 110',
        '4*x1 <= 88',
        '5*x1 <= 103',
        '8*x2 <= 110',
        '3*x2 <= 110',
        '8*x2 <= 88',
        '10*x2 <= 103',
        'x3 <= 110',
        '6*x3 <= 110',
        '9*x3 <= 88',
        '4*x3 <= 103',
        '3*x1 + 8*x2 + x3 >= 25',
        '3*x2 + 6*x3 >= 10',
        '4*x0 + 5*x1 >= 11',
        '4*x0 + 10*x2 >= 22',
        '10*x2 + 4*x3 >= 25',
        '4*x0 + 4*x3 >= 9',
        '4*x0 + 10*x2 + 4*x3 >= 22',
        '2*x0 - 6*x2 >= 0',
        '3*x1 + 8*x2 <= 104',
        '3*x1 + 8*x2 + x3 <= 107',
        '6*x0 + 8*x2 + x3 <= 50',
        '6*x0 + 3*x1 + x3 <= 104',
        '6*x0 + 3*x1 + 8*x2 <= 71',
        '6*x0 + 3*x1 + 8*x2 + x3 <= 71',
        'x0 + 8*x1 <= 104',
        'x0 + 6*x3 <= 80',
        '8*x1 + 6*x3 <= 35',
        'x0 + 3*x2 <= 86',
        '8*x1 + 3*x2 <= 57',
        'x0 + 8*x1 + 6*x3 <= 51',
        'x0 + 8*x1 + 3*x2 + 6*x3 <= 51',
        '8*x0 + 9*x3 <= 69',
        '4*x1 + 8*x2 <= 67',
        '8*x2 + 9*x3 <= 35',
        '8*x0 + 4*x1 <= 60',
        '8*x0 + 4*x1 + 8*x2 <= 82',
        '8*x0 + 4*x1 + 8*x2 + 9*x3 <= 82',
        '4*x0 + 4*x3 <= 39',
        '4*x0 + 5*x1 + 10*x2 + 4*x3 <= 39'
    ]
}
```

## Step 4: 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 B12
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x1")  # milligrams of magnesium
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x2")  # milligrams of vitamin E
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name="x3")  # milligrams of vitamin B5

    # Objective function
    model.setObjective(2 * x0 + 6 * x1 + 4 * x2 + 4 * x3, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6 * x0 <= 110)
    model.addConstr(x0 <= 110)
    model.addConstr(8 * x0 <= 88)
    model.addConstr(4 * x0 <= 103)
    model.addConstr(3 * x1 <= 110)
    model.addConstr(8 * x1 <= 110)
    model.addConstr(4 * x1 <= 88)
    model.addConstr(5 * x1 <= 103)
    model.addConstr(8 * x2 <= 110)
    model.addConstr(3 * x2 <= 110)
    model.addConstr(8 * x2 <= 88)
    model.addConstr(10 * x2 <= 103)
    model.addConstr(x3 <= 110)
    model.addConstr(6 * x3 <= 110)
    model.addConstr(9 * x3 <= 88)
    model.addConstr(4 * x3 <= 103)
    model.addConstr(3 * x1 + 8 * x2 + x3 >= 25)
    model.addConstr(3 * x2 + 6 * x3 >= 10)
    model.addConstr(4 * x0 + 5 * x1 >= 11)
    model.addConstr(4 * x0 + 10 * x2 >= 22)
    model.addConstr(10 * x2 + 4 * x3 >= 25)
    model.addConstr(4 * x0 + 4 * x3 >= 9)
    model.addConstr(4 * x0 + 10 * x2 + 4 * x3 >= 22)
    model.addConstr(2 * x0 - 6 * x2 >= 0)
    model.addConstr(3 * x1 + 8 * x2 <= 104)
    model.addConstr(3 * x1 + 8 * x2 + x3 <= 107)
    model.addConstr(6 * x0 + 8 * x2 + x3 <= 50)
    model.addConstr(6 * x0 + 3 * x1 + x3 <= 104)
    model.addConstr(6 * x0 + 3 * x1 + 8 * x2 <= 71)
    model.addConstr(6 * x0 + 3 * x1 + 8 * x2 + x3 <= 71)
    model.addConstr(x0 + 8 * x1 <= 104)
    model.addConstr(x0 + 6 * x3 <= 80)
    model.addConstr(8 * x1 + 6 * x3 <= 35)
    model.addConstr(x0 + 3 * x2 <= 86)
    model.addConstr(8 * x1 + 3 * x2 <= 57)
    model.addConstr(x0 + 8 * x1 + 6 * x3 <= 51)
    model.addConstr(x0 + 8 * x1 + 3 * x2 + 6 * x3 <= 51)
    model.addConstr(8 * x0 + 9 * x3 <= 69)
    model.addConstr(4 * x1 + 8 * x2 <= 67)
    model.addConstr(8 * x2 + 9 * x3 <= 35)
    model.addConstr(8 * x0 + 4 * x1 <= 60)
    model.addConstr(8 * x0 + 4 * x1 + 8 * x2 <= 82)
    model.addConstr(8 * x0 + 4 * x1 + 8 * x2 + 9 * x3 <= 82)
    model.addConstr(4 * x0 + 4 * x3 <= 39)
    model.addConstr(4 * x0 + 5 * x1 + 10 * x2 + 4 * x3 <= 39)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"milligrams of vitamin B12: {x0.varValue}")
        print(f"milligrams of magnesium: {x1.varValue}")
        print(f"milligrams of vitamin E: {x2.varValue}")
        print(f"milligrams of vitamin B5: {x3.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```