## Step 1: Define the symbolic representation of the variables
The variables are 'grams of carbohydrates' and 'milligrams of vitamin B4', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'grams of carbohydrates'
- $x_2$ for 'milligrams of vitamin B4'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $7x_1 + 1x_2$.

## 4: Translate the constraints into symbolic notation
The constraints are:
- $4x_1 + 8x_2 \geq 12$ (immune support index minimum)
- $1x_1 + 1x_2 \geq 13$ (cognitive performance index minimum)
- $6x_1 - 3x_2 \geq 0$
- $4x_1 + 8x_2 \leq 36$ (immune support index maximum)
- $1x_1 + 1x_2 \leq 16$ (cognitive performance index maximum)

## 5: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'grams of carbohydrates'), ('x2', 'milligrams of vitamin B4')],
    'objective_function': '7*x1 + 1*x2',
    'constraints': [
        '4*x1 + 8*x2 >= 12',
        '1*x1 + 1*x2 >= 13',
        '6*x1 - 3*x2 >= 0',
        '4*x1 + 8*x2 <= 36',
        '1*x1 + 1*x2 <= 16'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="grams_of_carbohydrates", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="milligrams_of_vitamin_B4", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(7 * x1 + x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(4 * x1 + 8 * x2 >= 12, name="immune_support_min")
    model.addConstr(x1 + x2 >= 13, name="cognitive_performance_min")
    model.addConstr(6 * x1 - 3 * x2 >= 0, name="carbohydrates_vitamin_B4_relation")
    model.addConstr(4 * x1 + 8 * x2 <= 36, name="immune_support_max")
    model.addConstr(x1 + x2 <= 16, name="cognitive_performance_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Grams of carbohydrates: {x1.varValue}")
        print(f"Milligrams of vitamin B4: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```