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

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ : grams of protein
- $x_2$ : milligrams of vitamin D

## 3: Define the objective function in symbolic notation
The objective function to maximize is $6.74x_1 + 7.11x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $11x_1 + 1x_2 \geq 60$
- $12x_1 + 19x_2 \geq 18$
- $6x_1 + 6x_2 \geq 18$
- $10x_1 - 3x_2 \geq 0$
- $11x_1 + 1x_2 \leq 87$
- $12x_1 + 19x_2 \leq 53$
- $6x_1 + 6x_2 \leq 39$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'grams of protein'), ('x2', 'milligrams of vitamin D')],
'objective_function': '6.74*x1 + 7.11*x2',
'constraints': [
    '11*x1 + 1*x2 >= 60',
    '12*x1 + 19*x2 >= 18',
    '6*x1 + 6*x2 >= 18',
    '10*x1 - 3*x2 >= 0',
    '11*x1 + 1*x2 <= 87',
    '12*x1 + 19*x2 <= 53',
    '6*x1 + 6*x2 <= 39'
]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="grams_of_protein", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="milligrams_of_vitamin_D", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(6.74 * x1 + 7.11 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(11 * x1 + x2 >= 60, name="immune_support_index")
    model.addConstr(12 * x1 + 19 * x2 >= 18, name="digestive_support_index")
    model.addConstr(6 * x1 + 6 * x2 >= 18, name="energy_stability_index")
    model.addConstr(10 * x1 - 3 * x2 >= 0, name="protein_vitamin_D_ratio")
    model.addConstr(11 * x1 + x2 <= 87, name="immune_support_index_upper_bound")
    model.addConstr(12 * x1 + 19 * x2 <= 53, name="digestive_support_index_upper_bound")
    model.addConstr(6 * x1 + 6 * x2 <= 39, name="energy_stability_index_upper_bound")

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```