## Step 1: Define the symbolic representation of the variables
The variables are 'cantaloupes' and 'peanutbutter sandwiches', which can be represented symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $3.2 \times \text{cantaloupes} + 2.05 \times \text{peanutbutter sandwiches}$. In symbolic terms, this is $3.2x_1 + 2.05x_2$.

## 3: Define the constraints in symbolic representation
1. $\text{cantaloupes}$ each contain 5 grams of fat: $5x_1$.
2. There are 4 grams of fat in $\text{peanutbutter sandwiches}$: $4x_2$.
3. At least 6 grams of fat from $\text{cantaloupes}$ plus $\text{peanutbutter sandwiches}$: $5x_1 + 4x_2 \geq 6$.
4. $5 \times \text{cantaloupes} + -9 \times \text{peanutbutter sandwiches} \geq 0$: $5x_1 - 9x_2 \geq 0$.
5. At most 33 grams of fat can come from $\text{cantaloupes}$ plus $\text{peanutbutter sandwiches}$: $5x_1 + 4x_2 \leq 33$.

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'cantaloupes'), ('x2', 'peanutbutter sandwiches')],
'objective_function': '3.2x1 + 2.05x2',
'constraints': [
    '5x1 + 4x2 >= 6',
    '5x1 - 9x2 >= 0',
    '5x1 + 4x2 <= 33'
]
}
```

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

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

    # Define the variables
    cantaloupes = model.addVar(name="cantaloupes", lb=0)
    peanutbutter_sandwiches = model.addVar(name="peanutbutter_sandwiches", lb=0)

    # Define the objective function
    model.setObjective(3.2 * cantaloupes + 2.05 * peanutbutter_sandwiches, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(5 * cantaloupes + 4 * peanutbutter_sandwiches >= 6, name="fat_min")
    model.addConstr(5 * cantaloupes - 9 * peanutbutter_sandwiches >= 0, name="cantaloupes_vs_sandwiches")
    model.addConstr(5 * cantaloupes + 4 * peanutbutter_sandwiches <= 33, name="fat_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cantaloupes: {cantaloupes.varValue}")
        print(f"Peanutbutter sandwiches: {peanutbutter_sandwiches.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```