## Step 1: Define the symbolic representation of the variables
The variables are 'bananas', 'cherry pies', 'bowls of instant ramen', 'apple pies', 'chicken thighs', which can be represented symbolically as $x_1, x_2, x_3, x_4, x_5$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $4 \times \text{bananas} + 6 \times \text{cherry pies} + 2 \times \text{bowls of instant ramen} + 6 \times \text{apple pies} + 9 \times \text{chicken thighs}$. In symbolic terms, this is $4x_1 + 6x_2 + 2x_3 + 6x_4 + 9x_5$.

## 3: Define the constraints in symbolic notation
The constraints given are:
1. $2x_1 + 3x_2 \geq 5$
2. $5x_4 + 2x_5 \geq 8$
3. $3x_2 + 2x_3 \geq 3$
4. $2x_1 + 3x_2 + 2x_5 \geq 6$
5. $2x_1 + 3x_2 + 2x_3 + 5x_4 + 2x_5 \geq 6$
6. $3x_2 + 2x_3 \leq 32$
7. $2x_3 + 5x_4 \leq 35$
8. $2x_1 + 2x_3 + 2x_5 \leq 33$
And the variables are integers: $x_1, x_2, x_3, x_4, x_5 \geq 0$ and are integers.

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'bananas'), 
        ('x2', 'cherry pies'), 
        ('x3', 'bowls of instant ramen'), 
        ('x4', 'apple pies'), 
        ('x5', 'chicken thighs')
    ], 
    'objective_function': '4*x1 + 6*x2 + 2*x3 + 6*x4 + 9*x5', 
    'constraints': [
        '2*x1 + 3*x2 >= 5',
        '5*x4 + 2*x5 >= 8',
        '3*x2 + 2*x3 >= 3',
        '2*x1 + 3*x2 + 2*x5 >= 6',
        '2*x1 + 3*x2 + 2*x3 + 5*x4 + 2*x5 >= 6',
        '3*x2 + 2*x3 <= 32',
        '2*x3 + 5*x4 <= 35',
        '2*x1 + 2*x3 + 2*x5 <= 33'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="bananas", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="cherry_pies", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="bowls_of_instant_ramen", vtype=gurobi.GRB.INTEGER)
    x4 = model.addVar(name="apple_pies", vtype=gurobi.GRB.INTEGER)
    x5 = model.addVar(name="chicken_thighs", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(4*x1 + 6*x2 + 2*x3 + 6*x4 + 9*x5, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(2*x1 + 3*x2 >= 5)
    model.addConstr(5*x4 + 2*x5 >= 8)
    model.addConstr(3*x2 + 2*x3 >= 3)
    model.addConstr(2*x1 + 3*x2 + 2*x5 >= 6)
    model.addConstr(2*x1 + 3*x2 + 2*x3 + 5*x4 + 2*x5 >= 6)
    model.addConstr(3*x2 + 2*x3 <= 32)
    model.addConstr(2*x3 + 5*x4 <= 35)
    model.addConstr(2*x1 + 2*x3 + 2*x5 <= 33)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Bananas: {x1.varValue}")
        print(f"Cherry pies: {x2.varValue}")
        print(f"Bowls of instant ramen: {x3.varValue}")
        print(f"Apple pies: {x4.varValue}")
        print(f"Chicken thighs: {x5.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```