To solve the given optimization problem, we first need to define the symbolic representation of the variables and the objective function. Then, we will express the constraints in terms of these symbolic variables.

### Symbolic Representation

Let's denote:
- \(x_1\) as 'milligrams of calcium'
- \(x_2\) as 'milligrams of vitamin B2'

The objective function to maximize is: \(3x_1 + 9x_2\)

The constraints are as follows:

1. Immune support index from milligrams of calcium and vitamin B2: \(18x_1 + 4x_2 \geq 66\) and \(18x_1 + 4x_2 \leq 133\)
2. Cognitive performance index from milligrams of calcium and vitamin B2: \(22x_1 + 10x_2 \geq 41\) and \(22x_1 + 10x_2 \leq 68\)
3. Digestive support index from milligrams of calcium and vitamin B2: \(20x_1 + 5x_2 \geq 61\) and \(20x_1 + 5x_2 \leq 95\)
4. Cardiovascular support index from milligrams of calcium and vitamin B2: \(16x_1 + 25x_2 \geq 43\) and \(16x_1 + 25x_2 \leq 222\)
5. Additional constraint: \(3x_1 - 8x_2 \geq 0\)

### Symbolic Representation in JSON Format

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

### Gurobi Code

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(name="milligrams_of_calcium", lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS)
x2 = m.addVar(name="milligrams_of_vitamin_B2", lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS)

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

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Milligrams of calcium: {x1.x}")
    print(f"Milligrams of vitamin B2: {x2.x}")
else:
    print("No optimal solution found.")
```