```json
{
  "sym_variables": [
    ("x1", "Combo 1"),
    ("x2", "Combo 2")
  ],
  "objective_function": "4*x1 + 4.5*x2",
  "constraints": [
    "25*x1 + 12*x2 <= 1200",
    "20*x1 + 21*x2 <= 1400",
    "15*x1 + 24*x2 <= 900",
    "x1 >= 0",
    "x2 >= 0"
  ]
}
```

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Candy_Combo_Optimization")

# Create decision variables
combo1 = model.addVar(vtype=gp.GRB.CONTINUOUS, name="combo1")
combo2 = model.addVar(vtype=gp.GRB.CONTINUOUS, name="combo2")


# Set objective function
model.setObjective(4 * combo1 + 4.5 * combo2, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(25 * combo1 + 12 * combo2 <= 1200, "gummy_bears_constraint")
model.addConstr(20 * combo1 + 21 * combo2 <= 1400, "gummy_worms_constraint")
model.addConstr(15 * combo1 + 24 * combo2 <= 900, "sour_candies_constraint")
model.addConstr(combo1 >=0)
model.addConstr(combo2 >=0)


# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of Combo 1: {combo1.x}")
    print(f"Number of Combo 2: {combo2.x}")
    print(f"Maximum Profit: ${model.objVal}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {model.status}")

```
