To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables for each item (cantaloupes and peanut butter sandwiches), formulating the objective function using these variables, and then listing all constraints as semi-algebraic expressions.

### Symbolic Representation:

Let's denote:
- $x_1$ as the number of cantaloupes,
- $x_2$ as the number of peanut butter sandwiches.

The objective function is to maximize: $3.2x_1 + 2.05x_2$

Constraints are:
1. $5x_1 + 4x_2 \geq 6$ (at least 6 grams of fat from cantaloupes and peanut butter sandwiches),
2. $5x_1 - 9x_2 \geq 0$ (the condition that $5$ times the number of cantaloupes plus $-9$ times the number of peanut butter sandwiches is at least zero),
3. $5x_1 + 4x_2 \leq 18$ (at most 18 grams of fat from cantaloupes and peanut butter sandwiches).

### Symbolic Problem Description:
```json
{
  'sym_variables': [('x1', 'cantaloupes'), ('x2', 'peanutbutter sandwiches')],
  'objective_function': '3.2*x1 + 2.05*x2',
  'constraints': [
    '5*x1 + 4*x2 >= 6',
    '5*x1 - 9*x2 >= 0',
    '5*x1 + 4*x2 <= 18'
  ]
}
```

### Gurobi Code:
To solve this problem using Gurobi, we'll write the following Python code:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables
x1 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="cantaloupes")
x2 = m.addVar(lb=0, ub=GRB.INFINITY, vtype=GRB.CONTINUOUS, name="peanutbutter_sandwiches")

# Define the objective function
m.setObjective(3.2*x1 + 2.05*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(5*x1 + 4*x2 >= 6, "fat_minimum")
m.addConstr(5*x1 - 9*x2 >= 0, "cantaloupes_vs_sandwiches")
m.addConstr(5*x1 + 4*x2 <= 18, "fat_maximum")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Cantaloupes: {x1.x}")
    print(f"Peanut butter sandwiches: {x2.x}")
else:
    print("No optimal solution found")
```