To solve this optimization problem using Gurobi, we need to define the decision variables, the objective function, and the constraints. The decision variables are the quantities of each food item: bananas, cherry pies, bowls of instant ramen, apple pies, and chicken thighs.

The objective function is given as:
4 * bananas + 6 * cherry_pies + 2 * bowls_of_instant_ramen + 6 * apple_pies + 9 * chicken_thighs

The constraints are based on the tastiness ratings and can be summarized as follows:

- Lower bound constraints for combined tastiness ratings.
- Upper bound constraints for combined tastiness ratings.
- All decision variables must be non-negative integers.

Let's translate these requirements into Gurobi code.

```python
from gurobipy import *

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

# Define the decision variables as integers
bananas = m.addVar(vtype=GRB.INTEGER, name="bananas")
cherry_pies = m.addVar(vtype=GRB.INTEGER, name="cherry_pies")
bowls_of_instant_ramen = m.addVar(vtype=GRB.INTEGER, name="bowls_of_instant_ramen")
apple_pies = m.addVar(vtype=GRB.INTEGER, name="apple_pies")
chicken_thighs = m.addVar(vtype=GRB.INTEGER, name="chicken_thighs")

# Objective function: minimize the total cost
m.setObjective(4 * bananas + 6 * cherry_pies + 2 * bowls_of_instant_ramen + 6 * apple_pies + 9 * chicken_thighs, GRB.MINIMIZE)

# Constraints:
# Lower bound constraints for combined tastiness ratings
m.addConstr(2 * bananas + 3 * cherry_pies >= 5, "tastiness_lower_bound_1")
m.addConstr(5 * apple_pies + 2 * chicken_thighs >= 8, "tastiness_lower_bound_2")
m.addConstr(3 * cherry_pies + 2 * bowls_of_instant_ramen >= 3, "tastiness_lower_bound_3")
m.addConstr(2 * bananas + 3 * cherry_pies + 2 * chicken_thighs >= 6, "tastiness_lower_bound_4")
m.addConstr(2 * bananas + 3 * cherry_pies + 2 * bowls_of_instant_ramen + 5 * apple_pies + 2 * chicken_thighs >= 6, "tastiness_lower_bound_5")

# Upper bound constraints for combined tastiness ratings
m.addConstr(3 * cherry_pies + 2 * bowls_of_instant_ramen <= 32, "tastiness_upper_bound_1")
m.addConstr(2 * bowls_of_instant_ramen + 5 * apple_pies <= 35, "tastiness_upper_bound_2")
m.addConstr(2 * bananas + 2 * bowls_of_instant_ramen + 2 * chicken_thighs <= 33, "tastiness_upper_bound_3")

# Solve the model
m.optimize()

# Print the solution if it exists
if m.status == GRB.OPTIMAL:
    print("Solution found:")
    print(f"Bananas: {bananas.x}")
    print(f"Cherry Pies: {cherry_pies.x}")
    print(f"Bowls of Instant Ramen: {bowls_of_instant_ramen.x}")
    print(f"Apple Pies: {apple_pies.x}")
    print(f"Chicken Thighs: {chicken_thighs.x}")
else:
    print("No solution found")
```
```python
```