## Step 1: Define the symbolic representation of the variables
The variables are 'milkshakes', 'cheeseburgers', and 'bananas', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ for 'milkshakes'
- $x_1$ for 'cheeseburgers'
- $x_2$ for 'bananas'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $8x_0 + 4x_1 + 4x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $x_0 \geq 0$ (Implicit, as quantity cannot be negative)
- $x_1 \geq 0$ (Implicit, as quantity cannot be negative)
- $x_2 \geq 0$ (Implicit, as quantity cannot be negative)
- $x_0 + 3x_1 + x_2 \geq 16$ (At least $16 spent)
- $11x_0 + 5x_2 \leq 31$ (At most 31 grams of fiber from milkshakes and bananas)
- $11x_0 + x_1 \leq 42$ (At most 42 grams of fiber from milkshakes and cheeseburgers)
- $x_1 + 5x_2 \leq 27$ (At most 27 grams of fiber from cheeseburgers and bananas)
- $11x_0 + x_1 + 5x_2 \leq 27$ (At most 27 grams of fiber from all)
- $x_0 + 3x_1 \leq 69$ (At most $69 spent on milkshakes and cheeseburgers)
- $x_0 + 3x_1 + x_2 \leq 69$ (At most $69 spent on all)
- $x_1$ is an integer (cheeseburgers must be a whole number)
- $x_2$ is an integer (bananas must be a whole number)

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'milkshakes'), ('x1', 'cheeseburgers'), ('x2', 'bananas')],
    'objective_function': '8*x0 + 4*x1 + 4*x2',
    'constraints': [
        'x0 + 3*x1 + x2 >= 16',
        '11*x0 + 5*x2 <= 31',
        '11*x0 + x1 <= 42',
        'x1 + 5*x2 <= 27',
        '11*x0 + x1 + 5*x2 <= 27',
        'x0 + 3*x1 <= 69',
        'x0 + 3*x1 + x2 <= 69'
    ]
}
```

## 6: 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
    milkshakes = model.addVar(lb=0, name="milkshakes", vtype=gurobi.GRB.CONTINUOUS)
    cheeseburgers = model.addVar(lb=0, vtype=gurobi.GRB.INTEGER, name="cheeseburgers")
    bananas = model.addVar(lb=0, vtype=gurobi.GRB.INTEGER, name="bananas")

    # Define the objective function
    model.setObjective(8 * milkshakes + 4 * cheeseburgers + 4 * bananas, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(milkshakes + 3 * cheeseburgers + bananas >= 16, name="min_spend")
    model.addConstr(11 * milkshakes + 5 * bananas <= 31, name="fiber_milkshakes_bananas")
    model.addConstr(11 * milkshakes + cheeseburgers <= 42, name="fiber_milkshakes_cheeseburgers")
    model.addConstr(cheeseburgers + 5 * bananas <= 27, name="fiber_cheeseburgers_bananas")
    model.addConstr(11 * milkshakes + cheeseburgers + 5 * bananas <= 27, name="fiber_all")
    model.addConstr(milkshakes + 3 * cheeseburgers <= 69, name="max_spend_milkshakes_cheeseburgers")
    model.addConstr(milkshakes + 3 * cheeseburgers + bananas <= 69, name="max_spend_all")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milkshakes: {milkshakes.varValue}")
        print(f"Cheeseburgers: {cheeseburgers.varValue}")
        print(f"Bananas: {bananas.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```