## 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 B12'
- $x_1$ represents 'milligrams of vitamin B5'
- $x_2$ represents 'milligrams of iron'

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $5x_0 + 9x_1 + 2x_2$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $6x_0 + 20x_1 + 11x_2 \leq 170$ (muscle growth index)
- $12x_0 + 9x_1 + 8x_2 \geq 23$ (energy stability index total)
- $6x_0 + 11x_2 \leq 98$ (muscle growth index for $x_0$ and $x_2$)
- $6x_0 + 20x_1 + 11x_2 \leq 98$ (muscle growth index total, redundant with the first constraint)
- $9x_1 + 8x_2 \geq 8$ (energy stability index for $x_1$ and $x_2$)
- $9x_1 + 8x_2 \leq 66$ (energy stability index for $x_1$ and $x_2$)
- $12x_0 + 9x_1 + 8x_2 \leq 66$ (energy stability index total)
- $13x_1 + 8x_2 \leq 106$ (cognitive performance index for $x_1$ and $x_2$)
- $7x_0 + 13x_1 \leq 67$ (cognitive performance index for $x_0$ and $x_1$)
- $7x_0 + 13x_1 + 8x_2 \leq 67$ (cognitive performance index total)

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B12'),
        ('x1', 'milligrams of vitamin B5'),
        ('x2', 'milligrams of iron')
    ],
    'objective_function': '5*x0 + 9*x1 + 2*x2',
    'constraints': [
        '6*x0 + 20*x1 + 11*x2 <= 170',
        '12*x0 + 9*x1 + 8*x2 >= 23',
        '6*x0 + 11*x2 <= 98',
        '9*x1 + 8*x2 >= 8',
        '9*x1 + 8*x2 <= 66',
        '12*x0 + 9*x1 + 8*x2 <= 66',
        '13*x1 + 8*x2 <= 106',
        '7*x0 + 13*x1 <= 67',
        '7*x0 + 13*x1 + 8*x2 <= 67'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B12
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B5
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of iron

    # Objective function
    model.setObjective(5 * x0 + 9 * x1 + 2 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(6 * x0 + 20 * x1 + 11 * x2 <= 170)  # muscle growth index
    model.addConstr(12 * x0 + 9 * x1 + 8 * x2 >= 23)  # energy stability index total
    model.addConstr(6 * x0 + 11 * x2 <= 98)  # muscle growth index for x0 and x2
    model.addConstr(9 * x1 + 8 * x2 >= 8)  # energy stability index for x1 and x2
    model.addConstr(9 * x1 + 8 * x2 <= 66)  # energy stability index for x1 and x2
    model.addConstr(12 * x0 + 9 * x1 + 8 * x2 <= 66)  # energy stability index total
    model.addConstr(13 * x1 + 8 * x2 <= 106)  # cognitive performance index for x1 and x2
    model.addConstr(7 * x0 + 13 * x1 <= 67)  # cognitive performance index for x0 and x1
    model.addConstr(7 * x0 + 13 * x1 + 8 * x2 <= 67)  # cognitive performance index total

    # 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 vitamin B5: {x1.varValue}")
        print(f"Milligrams of iron: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```