## Problem Description and Symbolic Representation

The problem can be broken down into the following symbolic representation:

### Variables
- $x_1$: Number of Combo 1 to sell
- $x_2$: Number of Combo 2 to sell

### Objective Function
Maximize profit: $4x_1 + 4.50x_2$

### Constraints
1. Gummy bears constraint: $25x_1 + 12x_2 \leq 1200$
2. Gummy worms constraint: $20x_1 + 21x_2 \leq 1400$
3. Sour candies constraint: $15x_1 + 24x_2 \leq 900$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'Combo 1'), ('x2', 'Combo 2')],
    'objective_function': '4*x1 + 4.50*x2',
    'constraints': [
        '25*x1 + 12*x2 <= 1200',
        '20*x1 + 21*x2 <= 1400',
        '15*x1 + 24*x2 <= 900',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="Combo_1")
    x2 = model.addVar(lb=0, name="Combo_2")

    # Objective function: Maximize profit
    model.setObjective(4 * x1 + 4.50 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(25 * x1 + 12 * x2 <= 1200, name="Gummy_Bears_Constraint")
    model.addConstr(20 * x1 + 21 * x2 <= 1400, name="Gummy_Worms_Constraint")
    model.addConstr(15 * x1 + 24 * x2 <= 900, name="Sour_Candies_Constraint")

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: Combo 1 = {x1.varValue}, Combo 2 = {x2.varValue}")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_combo_problem()
```