## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin C' and 'milligrams of vitamin B7'. Let's denote 'milligrams of vitamin C' as $x_1$ and 'milligrams of vitamin B7' as $x_2$. The objective function to maximize is $3.56x_1 + 9.33x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $5.67x_1 + 6.06x_2 \geq 84$ (cardiovascular support index)
- $0.73x_1 + 4.16x_2 \geq 21$ (digestive support index)
- $0.37x_1 + 1.26x_2 \geq 48$ (kidney support index)
- $3.56x_1 + 1.87x_2 \geq 58$ (energy stability index)
- $5x_1 - 3x_2 \geq 0$
- $5.67x_1 + 6.06x_2 \leq 248$ (cardiovascular support index upper bound)
- $0.73x_1 + 4.16x_2 \leq 90$ (digestive support index upper bound)
- $0.37x_1 + 1.26x_2 \leq 128$ (kidney support index upper bound)
- $3.56x_1 + 1.87x_2 \leq 127$ (energy stability index upper bound)

## 3: Provide the symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'milligrams of vitamin C'), ('x2', 'milligrams of vitamin B7')],
'objective_function': '3.56*x1 + 9.33*x2',
'constraints': [
    '5.67*x1 + 6.06*x2 >= 84',
    '0.73*x1 + 4.16*x2 >= 21',
    '0.37*x1 + 1.26*x2 >= 48',
    '3.56*x1 + 1.87*x2 >= 58',
    '5*x1 - 3*x2 >= 0',
    '5.67*x1 + 6.06*x2 <= 248',
    '0.73*x1 + 4.16*x2 <= 90',
    '0.37*x1 + 1.26*x2 <= 128',
    '3.56*x1 + 1.87*x2 <= 127'
]
}
```

## 4: 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_vitamin_C", lb=0)  # Assuming non-negative
    x2 = model.addVar(name="milligrams_of_vitamin_B7", lb=0)  # Assuming non-negative

    # Define the objective function
    model.setObjective(3.56 * x1 + 9.33 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(5.67 * x1 + 6.06 * x2 >= 84)
    model.addConstr(0.73 * x1 + 4.16 * x2 >= 21)
    model.addConstr(0.37 * x1 + 1.26 * x2 >= 48)
    model.addConstr(3.56 * x1 + 1.87 * x2 >= 58)
    model.addConstr(5 * x1 - 3 * x2 >= 0)
    model.addConstr(5.67 * x1 + 6.06 * x2 <= 248)
    model.addConstr(0.73 * x1 + 4.16 * x2 <= 90)
    model.addConstr(0.37 * x1 + 1.26 * x2 <= 128)
    model.addConstr(3.56 * x1 + 1.87 * x2 <= 127)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin C: {x1.varValue}")
        print(f"Milligrams of vitamin B7: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```