To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining the variables, the objective function, and the constraints using mathematical notation.

### Symbolic Representation

Let's denote:
- $x_1$ as the milligrams of vitamin B5,
- $x_2$ as the milligrams of vitamin B12.

The **objective function** to minimize is: $5x_1 + 5x_2$

The **constraints** are:
1. Cardiovascular support index from vitamin B5: $3x_1$
2. Cognitive performance index from vitamin B5: $11x_1$
3. Digestive support index from vitamin B5: $6x_1$
4. Cardiovascular support index from vitamin B12: $32x_2$
5. Cognitive performance index from vitamin B12: $10x_2$
6. Digestive support index from vitamin B12: $8x_2$
7. Minimum total cardiovascular support index: $3x_1 + 32x_2 \geq 32$
8. Minimum total cognitive performance index: $11x_1 + 10x_2 \geq 36$
9. Minimum total digestive support index: $6x_1 + 8x_2 \geq 28$
10. Constraint on vitamin B5 and B12 combination: $-10x_1 + 9x_2 \geq 0$
11. Maximum total cardiovascular support index: $3x_1 + 32x_2 \leq 44$
12. Maximum total cognitive performance index: $11x_1 + 10x_2 \leq 88$
13. Maximum total digestive support index: $6x_1 + 8x_2 \leq 86$

### Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'milligrams of vitamin B5'), ('x2', 'milligrams of vitamin B12')],
    'objective_function': '5*x1 + 5*x2',
    'constraints': [
        '3*x1 + 32*x2 >= 32',
        '11*x1 + 10*x2 >= 36',
        '6*x1 + 8*x2 >= 28',
        '-10*x1 + 9*x2 >= 0',
        '3*x1 + 32*x2 <= 44',
        '11*x1 + 10*x2 <= 88',
        '6*x1 + 8*x2 <= 86'
    ]
}
```

### Gurobi Code

```python
from gurobipy import *

# Create a new model
m = Model("Vitamin_Optimization")

# Define variables
x1 = m.addVar(name="milligrams_of_vitamin_B5", lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS)
x2 = m.addVar(name="milligrams_of_vitamin_B12", lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS)

# Set the objective function
m.setObjective(5*x1 + 5*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(3*x1 + 32*x2 >= 32, name="min_cardiovascular_support")
m.addConstr(11*x1 + 10*x2 >= 36, name="min_cognitive_performance")
m.addConstr(6*x1 + 8*x2 >= 28, name="min_digestive_support")
m.addConstr(-10*x1 + 9*x2 >= 0, name="vitamin_combination_constraint")
m.addConstr(3*x1 + 32*x2 <= 44, name="max_cardiovascular_support")
m.addConstr(11*x1 + 10*x2 <= 88, name="max_cognitive_performance")
m.addConstr(6*x1 + 8*x2 <= 86, name="max_digestive_support")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B5: {x1.x}")
    print(f"Milligrams of Vitamin B12: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```