## Step 1: Define the symbolic representation of the variables
The variables are ['apples', 'strips of bacon', 'slices of pizza', 'cornichons'] which can be represented symbolically as ['x0', 'x1', 'x2', 'x3'].

## Step 2: Create a dictionary mapping symbolic variables to their natural language counterparts
The symbolic variables and their corresponding natural language objects are:
- x0: apples
- x1: strips of bacon
- x2: slices of pizza
- x3: cornichons

## Step 3: Define the objective function in symbolic notation
The objective function to maximize is: $9.98x_0 + 3.57x_1 + 3.58x_2 + 8.96x_3$.

## 4: List all the constraints in symbolic notation
The constraints are:
- $9x_0 + 32x_1 + 35x_2 + 4x_3 \leq 391$ (total cost constraint)
- $9x_0 + 32x_1 \geq 78$ (apples and strips of bacon cost constraint)
- $3x_1 + 10x_3 \geq 39$ (tastiness rating from strips of bacon and cornichons)
- $34x_0 + 17x_2 \geq 39$ (tastiness rating from apples and slices of pizza)
- $3x_1 + 17x_2 \geq 21$ (tastiness rating from strips of bacon and slices of pizza)
- $35x_2 + 4x_3 \leq 310$ (cost constraint for slices of pizza and cornichons)
- $9x_0 + 32x_1 \leq 180$ (cost constraint for apples and strips of bacon)
- $32x_1 + 35x_2 \leq 214$ (cost constraint for strips of bacon and slices of pizza)
- $9x_0 + 4x_3 \leq 201$ (cost constraint for apples and cornichons)
- $9x_0 + 35x_2 \leq 325$ (cost constraint for apples and slices of pizza)
- $9x_0 + 32x_1 + 35x_2 + 4x_3 \leq 325$ (total cost constraint, redundant with the first constraint)
- $17x_2 + 10x_3 \leq 202$ (tastiness rating constraint for slices of pizza and cornichons)
- $34x_0 + 10x_3 \leq 213$ (tastiness rating constraint for apples and cornichons)
- $3x_1 + 10x_3 \leq 175$ (tastiness rating constraint for strips of bacon and cornichons)
- $34x_0 + 3x_1 \leq 135$ (tastiness rating constraint for apples and strips of bacon)
- $34x_0 + 3x_1 + 17x_2 + 10x_3 \leq 135$ (total tastiness rating constraint)

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'apples'), 
        ('x1', 'strips of bacon'), 
        ('x2', 'slices of pizza'), 
        ('x3', 'cornichons')
    ], 
    'objective_function': '9.98x0 + 3.57x1 + 3.58x2 + 8.96x3', 
    'constraints': [
        '9x0 + 32x1 + 35x2 + 4x3 <= 391',
        '9x0 + 32x1 >= 78',
        '3x1 + 10x3 >= 39',
        '34x0 + 17x2 >= 39',
        '3x1 + 17x2 >= 21',
        '35x2 + 4x3 <= 310',
        '9x0 + 32x1 <= 180',
        '32x1 + 35x2 <= 214',
        '9x0 + 4x3 <= 201',
        '9x0 + 35x2 <= 325',
        '9x0 + 32x1 + 35x2 + 4x3 <= 325',
        '17x2 + 10x3 <= 202',
        '34x0 + 10x3 <= 213',
        '3x1 + 10x3 <= 175',
        '34x0 + 3x1 <= 135',
        '34x0 + 3x1 + 17x2 + 10x3 <= 135'
    ]
}
```

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

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

# Define the variables
x0 = m.addVar(name="apples", lb=0)
x1 = m.addVar(name="strips of bacon", lb=0)
x2 = m.addVar(name="slices of pizza", lb=0)
x3 = m.addVar(name="cornichons", lb=0)

# Objective function
m.setObjective(9.98*x0 + 3.57*x1 + 3.58*x2 + 8.96*x3, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(9*x0 + 32*x1 + 35*x2 + 4*x3 <= 391)
m.addConstr(9*x0 + 32*x1 >= 78)
m.addConstr(3*x1 + 10*x3 >= 39)
m.addConstr(34*x0 + 17*x2 >= 39)
m.addConstr(3*x1 + 17*x2 >= 21)
m.addConstr(35*x2 + 4*x3 <= 310)
m.addConstr(9*x0 + 32*x1 <= 180)
m.addConstr(32*x1 + 35*x2 <= 214)
m.addConstr(9*x0 + 4*x3 <= 201)
m.addConstr(9*x0 + 35*x2 <= 325)
m.addConstr(9*x0 + 32*x1 + 35*x2 + 4*x3 <= 325)
m.addConstr(17*x2 + 10*x3 <= 202)
m.addConstr(34*x0 + 10*x3 <= 213)
m.addConstr(3*x1 + 10*x3 <= 175)
m.addConstr(34*x0 + 3*x1 <= 135)
m.addConstr(34*x0 + 3*x1 + 17*x2 + 10*x3 <= 135)

# Optimize the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", m.objVal)
    print("Apples: ", x0.varValue)
    print("Strips of bacon: ", x1.varValue)
    print("Slices of pizza: ", x2.varValue)
    print("Cornichons: ", x3.varValue)
else:
    print("The model is infeasible")
```