## Step 1: Define the symbolic representation of the variables
The variables are 'cherry pies', 'lemons', 'tomatoes', and 'cantaloupes', which can be represented symbolically as $x_1, x_2, x_3, x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'cherry pies'
- $x_2$ for 'lemons'
- $x_3$ for 'tomatoes'
- $x_4$ for 'cantaloupes'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $5.28x_1 + 5.35x_2 + 5.46x_3 + 3.47x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
- $3x_3 + 13x_4 \geq 125$
- $11x_1 + 18x_2 \geq 86$
- $18x_2 + 3x_3 \geq 107$
- $18x_2 + 13x_4 \geq 54$
- $11x_1 + 13x_4 \geq 103$
- $11x_1 + 3x_3 \leq 325$
- $18x_2 + 3x_3 \leq 415$
- $18x_2 + 13x_4 \leq 154$
- $11x_1 + 18x_2 + 3x_3 + 13x_4 \leq 518$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'cherry pies'),
        ('x2', 'lemons'),
        ('x3', 'tomatoes'),
        ('x4', 'cantaloupes')
    ],
    'objective_function': '5.28*x1 + 5.35*x2 + 5.46*x3 + 3.47*x4',
    'constraints': [
        '3*x3 + 13*x4 >= 125',
        '11*x1 + 18*x2 >= 86',
        '18*x2 + 3*x3 >= 107',
        '18*x2 + 13*x4 >= 54',
        '11*x1 + 13*x4 >= 103',
        '11*x1 + 3*x3 <= 325',
        '18*x2 + 3*x3 <= 415',
        '18*x2 + 13*x4 <= 154',
        '11*x1 + 18*x2 + 3*x3 + 13*x4 <= 518'
    ]
}
```

## 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
    x1 = model.addVar(name="cherry_pies", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="lemons", lb=0)     # No lower bound specified, assuming 0
    x3 = model.addVar(name="tomatoes", lb=0)  # No lower bound specified, assuming 0
    x4 = model.addVar(name="cantaloupes", lb=0) # No lower bound specified, assuming 0

    # Define the objective function
    model.setObjective(5.28 * x1 + 5.35 * x2 + 5.46 * x3 + 3.47 * x4, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x3 + 13 * x4 >= 125)
    model.addConstr(11 * x1 + 18 * x2 >= 86)
    model.addConstr(18 * x2 + 3 * x3 >= 107)
    model.addConstr(18 * x2 + 13 * x4 >= 54)
    model.addConstr(11 * x1 + 13 * x4 >= 103)
    model.addConstr(11 * x1 + 3 * x3 <= 325)
    model.addConstr(18 * x2 + 3 * x3 <= 415)
    model.addConstr(18 * x2 + 13 * x4 <= 154)
    model.addConstr(11 * x1 + 18 * x2 + 3 * x3 + 13 * x4 <= 518)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Cherry pies: {x1.varValue}")
        print(f"Lemons: {x2.varValue}")
        print(f"Tomatoes: {x3.varValue}")
        print(f"Cantaloupes: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```