To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(x_1\) as the number of Combo 1 deals sold,
- \(x_2\) as the number of Combo 2 deals sold.

The objective is to maximize profit. Given that the profit per Combo 1 is $4 and per Combo 2 is $4.50, the objective function can be written as:

\[ \text{Maximize} \quad 4x_1 + 4.5x_2 \]

We also have constraints based on the available quantities of each type of candy:

- For gummy bears: \(25x_1 + 12x_2 \leq 1200\),
- For gummy worms: \(20x_1 + 21x_2 \leq 1400\),
- For sour candies: \(15x_1 + 24x_2 \leq 900\).

Additionally, \(x_1\) and \(x_2\) must be non-negative since we cannot sell a negative number of combos.

Now, let's translate this problem into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the decision variables
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="Combo_1")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="Combo_2")

# Set the objective function
m.setObjective(4*x1 + 4.5*x2, GRB.MAXIMIZE)

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

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Number of Combo 1: {x1.x}")
    print(f"Number of Combo 2: {x2.x}")
    print(f"Maximum Profit: ${4*x1.x + 4.5*x2.x:.2f}")
else:
    print("No optimal solution found.")
```