## Step 1: Define the symbolic representation of the problem
First, let's define the symbolic variables and the objective function based on the given problem description.

## Step 2: Identify the variables and their corresponding symbolic representations
The variables are 'kale salads', 'ravioli', and 'apple pies', which we can represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## 3: Formulate the objective function
The objective function to maximize is $9x_0 + 8x_1 + 6x_2$.

## 4: List the constraints
The constraints given are:
- $9x_0 + 17x_1 + 2x_2 \geq 50$
- $3x_0 + 7x_1 + 20x_2 \geq 36$
- $9x_0 + 17x_1 \leq 163$
- $9x_0 + 17x_1 + 2x_2 \leq 83$
- $3x_0 + 7x_1 \leq 90$
- $7x_1 + 20x_2 \leq 151$
- $3x_0 + 20x_2 \leq 169$
- $3x_0 + 7x_1 + 20x_2 \leq 169$

## 5: Symbolic representation of the problem
The symbolic representation is:
```json
{
    'sym_variables': [
        ('x0', 'kale salads'),
        ('x1', 'ravioli'),
        ('x2', 'apple pies')
    ],
    'objective_function': '9*x0 + 8*x1 + 6*x2',
    'constraints': [
        '9*x0 + 17*x1 + 2*x2 >= 50',
        '3*x0 + 7*x1 + 20*x2 >= 36',
        '9*x0 + 17*x1 <= 163',
        '9*x0 + 17*x1 + 2*x2 <= 83',
        '3*x0 + 7*x1 <= 90',
        '7*x1 + 20*x2 <= 151',
        '3*x0 + 20*x2 <= 169',
        '3*x0 + 7*x1 + 20*x2 <= 169'
    ]
}
```

## Step 6: Gurobi code for the optimization problem
Now, let's write the Gurobi code to solve this optimization problem.

```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='kale_salads', lb=0)  # No upper bound for x0, x1, x2
    x1 = model.addVar(name='ravioli', lb=0)
    x2 = model.addVar(name='apple_pies', lb=0)

    # Define the objective function
    model.setObjective(9 * x0 + 8 * x1 + 6 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(9 * x0 + 17 * x1 + 2 * x2 >= 50)
    model.addConstr(3 * x0 + 7 * x1 + 20 * x2 >= 36)
    model.addConstr(9 * x0 + 17 * x1 <= 163)
    model.addConstr(9 * x0 + 17 * x1 + 2 * x2 <= 83)
    model.addConstr(3 * x0 + 7 * x1 <= 90)
    model.addConstr(7 * x1 + 20 * x2 <= 151)
    model.addConstr(3 * x0 + 20 * x2 <= 169)
    model.addConstr(3 * x0 + 7 * x1 + 20 * x2 <= 169)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('Kale salads: ', x0.varValue)
        print('Ravioli: ', x1.varValue)
        print('Apple pies: ', x2.varValue)
    else:
        print('The model is infeasible')

solve_optimization_problem()
```