## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B5' and 'milligrams of vitamin B12'. Let's denote 'milligrams of vitamin B5' as $x_1$ and 'milligrams of vitamin B12' as $x_2$. The objective function to minimize is $5x_1 + 5x_2$. The constraints are:
- $3x_1 + 32x_2 \geq 32$
- $11x_1 + 10x_2 \geq 36$
- $6x_1 + 8x_2 \geq 28$
- $-10x_1 + 9x_2 \geq 0$
- $3x_1 + 32x_2 \leq 44$
- $11x_1 + 10x_2 \leq 88$
- $6x_1 + 8x_2 \leq 86$

## 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B5'
- $x_2$ for 'milligrams of vitamin B12'

The objective function is: $5x_1 + 5x_2$

The constraints are:
1. $3x_1 + 32x_2 \geq 32$
2. $11x_1 + 10x_2 \geq 36$
3. $6x_1 + 8x_2 \geq 28$
4. $-10x_1 + 9x_2 \geq 0$
5. $3x_1 + 32x_2 \leq 44$
6. $11x_1 + 10x_2 \leq 88$
7. $6x_1 + 8x_2 \leq 86$

## 3: Express the problem in the required 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'
    ]
}
```

## 4: Implement the problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # milligrams of vitamin B5
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B12

    # Define the objective function
    model.setObjective(5 * x1 + 5 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(3 * x1 + 32 * x2 >= 32)
    model.addConstr(11 * x1 + 10 * x2 >= 36)
    model.addConstr(6 * x1 + 8 * x2 >= 28)
    model.addConstr(-10 * x1 + 9 * x2 >= 0)
    model.addConstr(3 * x1 + 32 * x2 <= 44)
    model.addConstr(11 * x1 + 10 * x2 <= 88)
    model.addConstr(6 * x1 + 8 * x2 <= 86)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```