## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of calcium' and 'milligrams of vitamin B2', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is $3x_1 + 9x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $18x_1 + 4x_2 \geq 66$
- $22x_1 + 10x_2 \geq 41$
- $20x_1 + 5x_2 \geq 61$
- $16x_1 + 25x_2 \geq 43$
- $3x_1 - 8x_2 \geq 0$
- $18x_1 + 4x_2 \leq 133$
- $22x_1 + 10x_2 \leq 68$
- $20x_1 + 5x_2 \leq 95$
- $16x_1 + 25x_2 \leq 222$

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [('x1', 'milligrams of calcium'), ('x2', 'milligrams of vitamin B2')],
    'objective_function': '3*x1 + 9*x2',
    'constraints': [
        '18*x1 + 4*x2 >= 66',
        '22*x1 + 10*x2 >= 41',
        '20*x1 + 5*x2 >= 61',
        '16*x1 + 25*x2 >= 43',
        '3*x1 - 8*x2 >= 0',
        '18*x1 + 4*x2 <= 133',
        '22*x1 + 10*x2 <= 68',
        '20*x1 + 5*x2 <= 95',
        '16*x1 + 25*x2 <= 222'
    ]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name='milligrams_of_calcium', lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name='milligrams_of_vitamin_B2', lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(3*x1 + 9*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(18*x1 + 4*x2 >= 66, name='immune_support_index_min')
    model.addConstr(22*x1 + 10*x2 >= 41, name='cognitive_performance_index_min')
    model.addConstr(20*x1 + 5*x2 >= 61, name='digestive_support_index_min')
    model.addConstr(16*x1 + 25*x2 >= 43, name='cardiovascular_support_index_min')
    model.addConstr(3*x1 - 8*x2 >= 0, name='calcium_vitamin_B2_ratio_min')
    model.addConstr(18*x1 + 4*x2 <= 133, name='immune_support_index_max')
    model.addConstr(22*x1 + 10*x2 <= 68, name='cognitive_performance_index_max')
    model.addConstr(20*x1 + 5*x2 <= 95, name='digestive_support_index_max')
    model.addConstr(16*x1 + 25*x2 <= 222, name='cardiovascular_support_index_max')

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'milligrams of calcium: {x1.varValue}')
        print(f'milligrams of vitamin B2: {x2.varValue}')
        print(f'Objective function value: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```