## Step 1: Define the optimization problem
The goal is to minimize the objective function: $8.6x_0 + 8.31x_1 + 4.72x_2 + 7.32x_3$, where $x_0$ represents the quantity of chicken drumsticks, $x_1$ represents the quantity of oranges, $x_2$ represents the quantity of bowls of cereal, and $x_3$ represents the quantity of bowls of pasta.

## Step 2: List all the constraints
The constraints are as follows:
1. $34x_0 + 26x_1 \geq 56$
2. $34x_0 + 23x_2 \geq 96$
3. $34x_0 + 26x_1 + 23x_2 + 35x_3 \geq 96$
4. $11x_0 + 29x_1 \geq 16$
5. $5x_2 + 10x_3 \geq 29$
6. $11x_0 + 5x_2 \geq 48$
7. $11x_0 + 29x_1 + 5x_2 + 10x_3 \geq 48$
8. $23x_0 + 14x_3 \geq 28$
9. $26x_1 + 14x_3 \geq 30$
10. $23x_0 + 17x_2 \geq 41$
11. $23x_0 + 26x_1 + 17x_2 + 14x_3 \geq 41$
12. $34x_0 + 26x_1 \leq 252$
13. $26x_1 + 35x_3 \leq 150$
14. $23x_2 + 35x_3 \leq 393$
15. $29x_1 + 5x_2 \leq 112$
16. $11x_0 + 5x_2 \leq 173$
17. $29x_1 + 10x_3 \leq 98$
18. $11x_0 + 10x_3 \leq 149$
19. $11x_0 + 29x_1 \leq 190$

## 3: Implement the optimization problem using Gurobi
We will use the Gurobi library in Python to model and solve this optimization problem.

```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="chicken_drumsticks", lb=0)
x1 = m.addVar(name="oranges", lb=0)
x2 = m.addVar(name="bowls_of_cereal", lb=0)
x3 = m.addVar(name="bowls_of_pasta", lb=0)

# Define the objective function
m.setObjective(8.6*x0 + 8.31*x1 + 4.72*x2 + 7.32*x3, gp.GRB.MINIMIZE)

# Define the constraints
m.addConstr(34*x0 + 26*x1 >= 56)
m.addConstr(34*x0 + 23*x2 >= 96)
m.addConstr(34*x0 + 26*x1 + 23*x2 + 35*x3 >= 96)
m.addConstr(11*x0 + 29*x1 >= 16)
m.addConstr(5*x2 + 10*x3 >= 29)
m.addConstr(11*x0 + 5*x2 >= 48)
m.addConstr(11*x0 + 29*x1 + 5*x2 + 10*x3 >= 48)
m.addConstr(23*x0 + 14*x3 >= 28)
m.addConstr(26*x1 + 14*x3 >= 30)
m.addConstr(23*x0 + 17*x2 >= 41)
m.addConstr(23*x0 + 26*x1 + 17*x2 + 14*x3 >= 41)
m.addConstr(34*x0 + 26*x1 <= 252)
m.addConstr(26*x1 + 35*x3 <= 150)
m.addConstr(23*x2 + 35*x3 <= 393)
m.addConstr(29*x1 + 5*x2 <= 112)
m.addConstr(11*x0 + 5*x2 <= 173)
m.addConstr(29*x1 + 10*x3 <= 98)
m.addConstr(11*x0 + 10*x3 <= 149)
m.addConstr(11*x0 + 29*x1 <= 190)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Chicken drumsticks: ", x0.varValue)
    print("Oranges: ", x1.varValue)
    print("Bowls of cereal: ", x2.varValue)
    print("Bowls of pasta: ", x3.varValue)
else:
    print("The model is infeasible")
```