## Step 1: Define the variables and their corresponding symbolic notation
Let's denote the variables as follows:
- $x_0$ = milligrams of calcium
- $x_1$ = grams of fat
- $x_2$ = milligrams of vitamin B9
- $x_3$ = milligrams of vitamin B1

## Step 2: Formulate the objective function
The objective function to minimize is: $8.95x_0 + 4.67x_1 + 9.76x_2 + 6.57x_3$

## Step 3: List all the constraints
### Digestive Support Index Constraints
1. $4x_0 + 6x_1 + 6x_2 + x_3 \geq 0$ is not directly given, but we have:
2. $4x_0 \leq 84$
3. $6x_1 \leq 84$
4. $6x_2 \leq 84$
5. $x_3 \leq 84$
6. $4x_0 + 6x_2 \geq 21$
7. $6x_1 + x_3 \geq 12$
8. $6x_2 + x_3 \geq 8$
9. $6x_1 + 6x_2 \geq 21$
10. $4x_0 + 6x_1 + x_3 \geq 13$
11. $4x_0 + 6x_2 + x_3 \geq 13$
12. $6x_1 + 6x_2 + x_3 \geq 13$
13. $4x_0 + 6x_1 + x_3 \geq 14$
14. $4x_0 + 6x_2 + x_3 \geq 14$
15. $6x_1 + 6x_2 + x_3 \geq 14$
16. $4x_0 + 6x_1 + x_3 \geq 15$
17. $4x_0 + 6x_2 + x_3 \geq 15$
18. $6x_1 + 6x_2 + x_3 \geq 15$
19. $4x_0 + 6x_1 + 6x_2 + x_3 \geq 15$

### Cognitive Performance Index Constraints
20. $3x_0 + 8x_3 \geq 6$
21. $5x_2 + 8x_3 \geq 7$
22. $4x_1 + 5x_2 \geq 12$
23. $3x_0 + 5x_2 + 8x_3 \geq 18$
24. $3x_0 + 4x_1 + 5x_2 + 8x_3 \geq 18$

### Cardiovascular Support Index Constraints
25. $8x_1 + 4x_3 \geq 19$
26. $3x_0 + 8x_1 \geq 17$
27. $8x_1 + 6x_2 \geq 8$
28. $6x_2 + 4x_3 \geq 20$
29. $3x_0 + 6x_2 + 4x_3 \geq 14$
30. $3x_0 + 8x_1 + 4x_3 \geq 14$
31. $3x_0 + 8x_1 + 6x_2 \geq 14$
32. $3x_0 + 6x_2 + 4x_3 \geq 13$
33. $3x_0 + 8x_1 + 4x_3 \geq 13$
34. $3x_0 + 8x_1 + 6x_2 \geq 13$
35. $3x_0 + 6x_2 + 4x_3 \geq 11$
36. $3x_0 + 8x_1 + 4x_3 \geq 11$
37. $3x_0 + 8x_1 + 6x_2 \geq 11$
38. $3x_0 + 8x_1 + 6x_2 + 4x_3 \geq 11$

### Other Constraints
39. $10x_0 - 5x_1 \geq 0$
40. $10x_1 - 8x_3 \geq 0$
41. $4x_0 + 6x_2 + x_3 \leq 69$
42. $6x_1 + 6x_2 + x_3 \leq 49$
43. $4x_0 + 6x_1 + 6x_2 \leq 66$
44. $4x_0 + 6x_1 + x_3 \leq 27$
45. $3x_0 + 4x_1 \leq 55$
46. $3x_0 + 4x_1 + 5x_2 \leq 44$
47. $4x_1 + 5x_2 + 8x_3 \leq 62$
48. $3x_0 + 4x_3 \leq 23$
49. $3x_0 + 6x_2 \leq 60$
50. $3x_0 + 8x_1 + 4x_3 \leq 61$

## Step 4: Symbolic Representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of calcium'),
        ('x1', 'grams of fat'),
        ('x2', 'milligrams of vitamin B9'),
        ('x3', 'milligrams of vitamin B1')
    ],
    'objective_function': '8.95*x0 + 4.67*x1 + 9.76*x2 + 6.57*x3',
    'constraints': [
        '4*x0 <= 84',
        '6*x1 <= 84',
        '6*x2 <= 84',
        'x3 <= 84',
        '4*x0 + 6*x2 >= 21',
        '6*x1 + x3 >= 12',
        '6*x2 + x3 >= 8',
        '6*x1 + 6*x2 >= 21',
        '4*x0 + 6*x1 + x3 >= 13',
        '4*x0 + 6*x2 + x3 >= 13',
        '6*x1 + 6*x2 + x3 >= 13',
        '4*x0 + 6*x1 + x3 >= 14',
        '4*x0 + 6*x2 + x3 >= 14',
        '6*x1 + 6*x2 + x3 >= 14',
        '4*x0 + 6*x1 + x3 >= 15',
        '4*x0 + 6*x2 + x3 >= 15',
        '6*x1 + 6*x2 + x3 >= 15',
        '4*x0 + 6*x1 + 6*x2 + x3 >= 15',
        '3*x0 + 8*x3 >= 6',
        '5*x2 + 8*x3 >= 7',
        '4*x1 + 5*x2 >= 12',
        '3*x0 + 5*x2 + 8*x3 >= 18',
        '3*x0 + 4*x1 + 5*x2 + 8*x3 >= 18',
        '8*x1 + 4*x3 >= 19',
        '3*x0 + 8*x1 >= 17',
        '8*x1 + 6*x2 >= 8',
        '6*x2 + 4*x3 >= 20',
        '3*x0 + 6*x2 + 4*x3 >= 14',
        '3*x0 + 8*x1 + 4*x3 >= 14',
        '3*x0 + 8*x1 + 6*x2 >= 14',
        '3*x0 + 6*x2 + 4*x3 >= 13',
        '3*x0 + 8*x1 + 4*x3 >= 13',
        '3*x0 + 8*x1 + 6*x2 >= 13',
        '3*x0 + 6*x2 + 4*x3 >= 11',
        '3*x0 + 8*x1 + 4*x3 >= 11',
        '3*x0 + 8*x1 + 6*x2 >= 11',
        '3*x0 + 8*x1 + 6*x2 + 4*x3 >= 11',
        '10*x0 - 5*x1 >= 0',
        '10*x1 - 8*x3 >= 0',
        '4*x0 + 6*x2 + x3 <= 69',
        '6*x1 + 6*x2 + x3 <= 49',
        '4*x0 + 6*x1 + 6*x2 <= 66',
        '4*x0 + 6*x1 + x3 <= 27',
        '3*x0 + 4*x1 <= 55',
        '3*x0 + 4*x1 + 5*x2 <= 44',
        '4*x1 + 5*x2 + 8*x3 <= 62',
        '3*x0 + 4*x3 <= 23',
        '3*x0 + 6*x2 <= 60',
        '3*x0 + 8*x1 + 4*x3 <= 61'
    ]
}
```

## Step 5: Gurobi Code
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of calcium")
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, name="grams of fat")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of vitamin B9")
    x3 = model.addVar(lb=-gurobi.GRB.INFINITY, name="milligrams of vitamin B1")

    # Objective function
    model.setObjective(8.95 * x0 + 4.67 * x1 + 9.76 * x2 + 6.57 * x3, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(4 * x0 <= 84)
    model.addConstr(6 * x1 <= 84)
    model.addConstr(6 * x2 <= 84)
    model.addConstr(x3 <= 84)
    model.addConstr(4 * x0 + 6 * x2 >= 21)
    model.addConstr(6 * x1 + x3 >= 12)
    model.addConstr(6 * x2 + x3 >= 8)
    model.addConstr(6 * x1 + 6 * x2 >= 21)
    model.addConstr(4 * x0 + 6 * x1 + x3 >= 13)
    model.addConstr(4 * x0 + 6 * x2 + x3 >= 13)
    model.addConstr(6 * x1 + 6 * x2 + x3 >= 13)
    model.addConstr(4 * x0 + 6 * x1 + x3 >= 14)
    model.addConstr(4 * x0 + 6 * x2 + x3 >= 14)
    model.addConstr(6 * x1 + 6 * x2 + x3 >= 14)
    model.addConstr(4 * x0 + 6 * x1 + x3 >= 15)
    model.addConstr(4 * x0 + 6 * x2 + x3 >= 15)
    model.addConstr(6 * x1 + 6 * x2 + x3 >= 15)
    model.addConstr(4 * x0 + 6 * x1 + 6 * x2 + x3 >= 15)
    model.addConstr(3 * x0 + 8 * x3 >= 6)
    model.addConstr(5 * x2 + 8 * x3 >= 7)
    model.addConstr(4 * x1 + 5 * x2 >= 12)
    model.addConstr(3 * x0 + 5 * x2 + 8 * x3 >= 18)
    model.addConstr(3 * x0 + 4 * x1 + 5 * x2 + 8 * x3 >= 18)
    model.addConstr(8 * x1 + 4 * x3 >= 19)
    model.addConstr(3 * x0 + 8 * x1 >= 17)
    model.addConstr(8 * x1 + 6 * x2 >= 8)
    model.addConstr(6 * x2 + 4 * x3 >= 20)
    model.addConstr(3 * x0 + 6 * x2 + 4 * x3 >= 14)
    model.addConstr(3 * x0 + 8 * x1 + 4 * x3 >= 14)
    model.addConstr(3 * x0 + 8 * x1 + 6 * x2 >= 14)
    model.addConstr(3 * x0 + 6 * x2 + 4 * x3 >= 13)
    model.addConstr(3 * x0 + 8 * x1 + 4 * x3 >= 13)
    model.addConstr(3 * x0 + 8 * x1 + 6 * x2 >= 13)
    model.addConstr(3 * x0 + 6 * x2 + 4 * x3 >= 11)
    model.addConstr(3 * x0 + 8 * x1 + 4 * x3 >= 11)
    model.addConstr(3 * x0 + 8 * x1 + 6 * x2 >= 11)
    model.addConstr(3 * x0 + 8 * x1 + 6 * x2 + 4 * x3 >= 11)
    model.addConstr(10 * x0 - 5 * x1 >= 0)
    model.addConstr(10 * x1 - 8 * x3 >= 0)
    model.addConstr(4 * x0 + 6 * x2 + x3 <= 69)
    model.addConstr(6 * x1 + 6 * x2 + x3 <= 49)
    model.addConstr(4 * x0 + 6 * x1 + 6 * x2 <= 66)
    model.addConstr(4 * x0 + 6 * x1 + x3 <= 27)
    model.addConstr(3 * x0 + 4 * x1 <= 55)
    model.addConstr(3 * x0 + 4 * x1 + 5 * x2 <= 44)
    model.addConstr(4 * x1 + 5 * x2 + 8 * x3 <= 62)
    model.addConstr(3 * x0 + 4 * x3 <= 23)
    model.addConstr(3 * x0 + 6 * x2 <= 60)
    model.addConstr(3 * x0 + 8 * x1 + 4 * x3 <= 61)

    model.optimize()

    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print("Milligrams of calcium: ", x0.varValue)
        print("Grams of fat: ", x1.varValue)
        print("Milligrams of vitamin B9: ", x2.varValue)
        print("Milligrams of vitamin B1: ", x3.varValue)
        print("Objective function value: ", model.objVal)
    else:
        print("No optimal solution found.")

optimization_problem()
```