## Step 1: Define the symbolic representation of the variables
The variables are ['grams of fat', 'milligrams of vitamin B9', 'milligrams of vitamin C', 'grams of protein'] which can be represented symbolically as 'x1', 'x2', 'x3', 'x4' respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- 'x1' for 'grams of fat'
- 'x2' for 'milligrams of vitamin B9'
- 'x3' for 'milligrams of vitamin C'
- 'x4' for 'grams of protein'

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is: $6.81x_1 + 7.65x_2 + 1.37x_3 + 2.1x_4$

## 4: List the constraints in symbolic notation
The constraints are:
- $2.67x_1 \leq 57$
- $4.62x_2 \leq 57$
- $1.83x_3 \leq 57$
- $0.06x_4 \leq 57$
- $4.62x_2 + 0.06x_4 \geq 8$
- $2.67x_1 + 4.62x_2 \geq 12$
- $4.62x_2 + 1.83x_3 \geq 12$
- $2.67x_1 + 1.83x_3 \geq 14$
- $2.67x_1 + 1.83x_3 + 0.06x_4 \geq 14$
- $5.1x_3 + 5.93x_4 \leq 83$
- $0.05x_2 + 5.93x_4 \leq 60$
- $6.54x_1 + 5.1x_3 \leq 57$
- $6.54x_1 + 0.05x_2 + 5.1x_3 + 5.93x_4 \leq 57$
- $2.67x_1 + 0.06x_4 \leq 14$
- $2.67x_1 + 1.83x_3 \leq 49$
- $4.62x_2 + 1.83x_3 \leq 40$
- $4.62x_2 + 0.06x_4 \leq 57$
- $1.83x_3 + 0.06x_4 \leq 51$
- $2.67x_1 + 4.62x_2 \leq 51$
- $2.67x_1 + 4.62x_2 + 0.06x_4 \leq 41$
- $2.67x_1 + 4.62x_2 + 1.83x_3 + 0.06x_4 \leq 41$

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x1', 'grams of fat'),
        ('x2', 'milligrams of vitamin B9'),
        ('x3', 'milligrams of vitamin C'),
        ('x4', 'grams of protein')
    ],
    'objective_function': '6.81x1 + 7.65x2 + 1.37x3 + 2.1x4',
    'constraints': [
        '2.67x1 <= 57',
        '4.62x2 <= 57',
        '1.83x3 <= 57',
        '0.06x4 <= 57',
        '4.62x2 + 0.06x4 >= 8',
        '2.67x1 + 4.62x2 >= 12',
        '4.62x2 + 1.83x3 >= 12',
        '2.67x1 + 1.83x3 >= 14',
        '2.67x1 + 1.83x3 + 0.06x4 >= 14',
        '5.1x3 + 5.93x4 <= 83',
        '0.05x2 + 5.93x4 <= 60',
        '6.54x1 + 5.1x3 <= 57',
        '6.54x1 + 0.05x2 + 5.1x3 + 5.93x4 <= 57',
        '2.67x1 + 0.06x4 <= 14',
        '2.67x1 + 1.83x3 <= 49',
        '4.62x2 + 1.83x3 <= 40',
        '4.62x2 + 0.06x4 <= 57',
        '1.83x3 + 0.06x4 <= 51',
        '2.67x1 + 4.62x2 <= 51',
        '2.67x1 + 4.62x2 + 0.06x4 <= 41',
        '2.67x1 + 4.62x2 + 1.83x3 + 0.06x4 <= 41'
    ]
}
```

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

def optimize_problem():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="x1", lb=0)  # grams of fat
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B9
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin C
    x4 = model.addVar(name="x4", lb=0)  # grams of protein

    # Objective function
    model.setObjective(6.81 * x1 + 7.65 * x2 + 1.37 * x3 + 2.1 * x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(2.67 * x1 <= 57)
    model.addConstr(4.62 * x2 <= 57)
    model.addConstr(1.83 * x3 <= 57)
    model.addConstr(0.06 * x4 <= 57)
    model.addConstr(4.62 * x2 + 0.06 * x4 >= 8)
    model.addConstr(2.67 * x1 + 4.62 * x2 >= 12)
    model.addConstr(4.62 * x2 + 1.83 * x3 >= 12)
    model.addConstr(2.67 * x1 + 1.83 * x3 >= 14)
    model.addConstr(2.67 * x1 + 1.83 * x3 + 0.06 * x4 >= 14)
    model.addConstr(5.1 * x3 + 5.93 * x4 <= 83)
    model.addConstr(0.05 * x2 + 5.93 * x4 <= 60)
    model.addConstr(6.54 * x1 + 5.1 * x3 <= 57)
    model.addConstr(6.54 * x1 + 0.05 * x2 + 5.1 * x3 + 5.93 * x4 <= 57)
    model.addConstr(2.67 * x1 + 0.06 * x4 <= 14)
    model.addConstr(2.67 * x1 + 1.83 * x3 <= 49)
    model.addConstr(4.62 * x2 + 1.83 * x3 <= 40)
    model.addConstr(4.62 * x2 + 0.06 * x4 <= 57)
    model.addConstr(1.83 * x3 + 0.06 * x4 <= 51)
    model.addConstr(2.67 * x1 + 4.62 * x2 <= 51)
    model.addConstr(2.67 * x1 + 4.62 * x2 + 0.06 * x4 <= 41)
    model.addConstr(2.67 * x1 + 4.62 * x2 + 1.83 * x3 + 0.06 * x4 <= 41)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Objective: {model.objval}")
        print(f"x1 (grams of fat): {x1.varValue}")
        print(f"x2 (milligrams of vitamin B9): {x2.varValue}")
        print(f"x3 (milligrams of vitamin C): {x3.varValue}")
        print(f"x4 (grams of protein): {x4.varValue}")
    else:
        print("No optimal solution found.")

optimize_problem()
```