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

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : cheeseburgers
- $x_1$ : oreos
- $x_2$ : bananas

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is $3x_0 + 3x_1 + 1x_2$.

## Step 4: List the constraints in symbolic notation
The constraints are:
- $x_0 + 2x_1 \geq 8$ (total combined grams of protein from cheeseburgers plus oreos)
- $3x_1 + 5x_2 \geq 46$ (total combined tastiness rating from oreos plus bananas)
- $8x_0 + 5x_2 \geq 35$ (total combined tastiness rating from cheeseburgers and bananas)
- $x_0 + 7x_2 \leq 60$ (total combined grams of protein from cheeseburgers plus bananas)
- $x_0 + 2x_1 \leq 47$ (total combined grams of protein from cheeseburgers and oreos)
- $x_0 + 2x_1 + 7x_2 \leq 47$ (total combined grams of protein from cheeseburgers plus oreos plus bananas)
- $3x_1 + 5x_2 \leq 113$ (total combined tastiness rating from oreos and bananas)
- $8x_0 + 5x_2 \leq 105$ (total combined tastiness rating from cheeseburgers and bananas)
- $8x_0 + 3x_1 + 5x_2 \leq 105$ (total combined tastiness rating from cheeseburgers plus oreos plus bananas)

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'cheeseburgers'), ('x1', 'oreos'), ('x2', 'bananas')], 
    'objective_function': '3*x0 + 3*x1 + x2', 
    'constraints': [
        'x0 + 2*x1 >= 8', 
        '3*x1 + 5*x2 >= 46', 
        '8*x0 + 5*x2 >= 35', 
        'x0 + 7*x2 <= 60', 
        'x0 + 2*x1 <= 47', 
        'x0 + 2*x1 + 7*x2 <= 47', 
        '3*x1 + 5*x2 <= 113', 
        '8*x0 + 5*x2 <= 105', 
        '8*x0 + 3*x1 + 5*x2 <= 105'
    ]
}
```

## 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
    x0 = model.addVar(name="cheeseburgers", lb=0)  # No lower bound specified, assuming 0
    x1 = model.addVar(name="oreos", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="bananas", lb=0)  # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(3*x0 + 3*x1 + x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x0 + 2*x1 >= 8)
    model.addConstr(3*x1 + 5*x2 >= 46)
    model.addConstr(8*x0 + 5*x2 >= 35)
    model.addConstr(x0 + 7*x2 <= 60)
    model.addConstr(x0 + 2*x1 <= 47)
    model.addConstr(x0 + 2*x1 + 7*x2 <= 47)
    model.addConstr(3*x1 + 5*x2 <= 113)
    model.addConstr(8*x0 + 5*x2 <= 105)
    model.addConstr(8*x0 + 3*x1 + 5*x2 <= 105)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cheeseburgers: {x0.varValue}")
        print(f"Oreos: {x1.varValue}")
        print(f"Bananas: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```