## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B9' and 'milligrams of vitamin B5'. Let's denote 'milligrams of vitamin B9' as $x_1$ and 'milligrams of vitamin B5' as $x_2$. The objective function to maximize is $1.18x_1 + 8.37x_2$.

## Step 2: List the constraints in symbolic notation
The constraints given are:
- $7x_1 + 5x_2 \geq 31$ (immune support index)
- $14x_1 + 13x_2 \geq 21$ (energy stability index)
- $4x_1 + 6x_2 \geq 42$ (digestive support index)
- $-7x_1 + 8x_2 \geq 0$
- $7x_1 + 5x_2 \leq 101$ (immune support index upper bound)
- $14x_1 + 13x_2 \leq 61$ (energy stability index upper bound)
- $4x_1 + 6x_2 \leq 113$ (digestive support index upper bound)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity, though not explicitly stated, implied by context)

## 3: Provide the symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B9'), ('x2', 'milligrams of vitamin B5')],
'objective_function': '1.18*x1 + 8.37*x2',
'constraints': [
    '7*x1 + 5*x2 >= 31',
    '14*x1 + 13*x2 >= 21',
    '4*x1 + 6*x2 >= 42',
    '-7*x1 + 8*x2 >= 0',
    '7*x1 + 5*x2 <= 101',
    '14*x1 + 13*x2 <= 61',
    '4*x1 + 6*x2 <= 113'
]
}
```

## 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_B9", lb=0)
    x2 = model.addVar(name="milligrams_of_vitamin_B5", lb=0)

    # Define the objective function
    model.setObjective(1.18 * x1 + 8.37 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(7 * x1 + 5 * x2 >= 31)
    model.addConstr(14 * x1 + 13 * x2 >= 21)
    model.addConstr(4 * x1 + 6 * x2 >= 42)
    model.addConstr(-7 * x1 + 8 * x2 >= 0)
    model.addConstr(7 * x1 + 5 * x2 <= 101)
    model.addConstr(14 * x1 + 13 * x2 <= 61)
    model.addConstr(4 * x1 + 6 * x2 <= 113)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B9: {x1.varValue}")
        print(f"Milligrams of vitamin B5: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```