## Step 1: Define the symbolic representation of the variables
The variables are 'ravioli', 'slices of pizza', and 'apple pies', which we will represent symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic variables
The symbolic representation of the variables is:
- $x_0$ : ravioli
- $x_1$ : slices of pizza
- $x_2$ : apple pies

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $1x_0 + 9x_1 + 1x_2$

## 4: List the constraints in symbolic notation
The constraints are:
- $5x_0 + 1x_2 \geq 42$
- $6x_1 + 1x_2 \geq 18$
- $5x_0 + 6x_1 + 1x_2 \geq 39$
- $11x_0 + 8x_1 \leq 38$
- $11x_0 + 9x_2 \leq 84$
- $11x_0 + 8x_1 + 9x_2 \leq 84$
- $6x_1 + 1x_2 \leq 114$
- $5x_0 + 1x_2 \leq 153$
- $5x_0 + 6x_1 + 1x_2 \leq 153$

## 5: Provide the symbolic representation of the problem
```json
{
'sym_variables': [('x0', 'ravioli'), ('x1', 'slices of pizza'), ('x2', 'apple pies')],
'objective_function': '1*x0 + 9*x1 + 1*x2',
'constraints': [
    '5*x0 + 1*x2 >= 42',
    '6*x1 + 1*x2 >= 18',
    '5*x0 + 6*x1 + 1*x2 >= 39',
    '11*x0 + 8*x1 <= 38',
    '11*x0 + 9*x2 <= 84',
    '11*x0 + 8*x1 + 9*x2 <= 84',
    '6*x1 + 1*x2 <= 114',
    '5*x0 + 1*x2 <= 153',
    '5*x0 + 6*x1 + 1*x2 <= 153'
]
}
```

## Step 6: Write the Gurobi code to solve the optimization problem
```python
import gurobi

# Create a new model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="ravioli", lb=0)  # ravioli
x1 = m.addVar(name="slices of pizza", lb=0)  # slices of pizza
x2 = m.addVar(name="apple pies", lb=0)  # apple pies

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

# Define the constraints
m.addConstr(5 * x0 + x2 >= 42, name="umami_ravioli_apple_pies")
m.addConstr(6 * x1 + x2 >= 18, name="umami_pizza_apple_pies")
m.addConstr(5 * x0 + 6 * x1 + x2 >= 39, name="umami_total")
m.addConstr(11 * x0 + 8 * x1 <= 38, name="healthiness_ravioli_pizza")
m.addConstr(11 * x0 + 9 * x2 <= 84, name="healthiness_ravioli_apple_pies")
m.addConstr(11 * x0 + 8 * x1 + 9 * x2 <= 84, name="healthiness_total")
m.addConstr(6 * x1 + x2 <= 114, name="umami_pizza_apple_pies_upper")
m.addConstr(5 * x0 + x2 <= 153, name="umami_ravioli_apple_pies_upper")
m.addConstr(5 * x0 + 6 * x1 + x2 <= 153, name="umami_total_upper")

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Ravioli: ", x0.varValue)
    print("Slices of pizza: ", x1.varValue)
    print("Apple pies: ", x2.varValue)
else:
    print("The model is infeasible")
```