## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- 'x1' for 'corn cobs'
- 'x2' for 'sashimi'
- 'x3' for 'cornichons'
- 'x4' for 'strips of bacon'
- 'x5' for 'apple pies'

## Step 2: Define the objective function in symbolic notation
The objective function to maximize is: $7.63x_1 + 8.79x_2 + 2.61x_3 + 7.13x_4 + 2.17x_5$

## Step 3: List the constraints in symbolic notation
Constraints:
1. $4.64x_1 + 4.68x_2 + 0.74x_3 + 1.55x_4 + 3.84x_5 \leq 232$
2. $0.74x_3 + 3.84x_5 \geq 19$
3. $4.68x_2 + 3.84x_5 \geq 26$
4. $4.68x_2 + 0.74x_3 \geq 24$
5. $4.68x_2 + 1.55x_4 \geq 38$
6. $4.64x_1 + 3.84x_5 \geq 15$
7. $4.64x_1 + 4.68x_2 + 0.74x_3 \geq 40$
8. $4.64x_1 + 4.68x_2 + 3.84x_5 \geq 40$
9. $4.64x_1 + 1.55x_4 + 3.84x_5 \geq 40$
10. $4.64x_1 + 0.74x_3 + 1.55x_4 \geq 40$
11. $4.64x_1 + 4.68x_2 + 0.74x_3 \geq 38$
12. $4.64x_1 + 4.68x_2 + 3.84x_5 \geq 38$
13. $4.64x_1 + 1.55x_4 + 3.84x_5 \geq 38$
14. $4.64x_1 + 0.74x_3 + 1.55x_4 \geq 38$
15. $4.64x_1 + 4.68x_2 + 0.74x_3 \geq 33$
16. $4.64x_1 + 4.68x_2 + 3.84x_5 \geq 33$
17. $4.64x_1 + 1.55x_4 + 3.84x_5 \geq 33$
18. $4.64x_1 + 0.74x_3 + 1.55x_4 \geq 33$
19. $4.64x_1 + 4.68x_2 + 0.74x_3 \geq 26$
20. $4.64x_1 + 4.68x_2 + 3.84x_5 \geq 26$
21. $4.64x_1 + 1.55x_4 + 3.84x_5 \geq 26$
22. $4.64x_1 + 0.74x_3 + 1.55x_4 \geq 26$
23. $7x_1 - 5x_2 + 9x_3 \geq 0$
24. $4.68x_2 + 0.74x_3 \leq 105$
25. $4.68x_2 + 3.84x_5 \leq 117$
26. $0.74x_3 + 1.55x_4 \leq 206$
27. $4.68x_2 + 0.74x_3 + 3.84x_5 \leq 85$
28. $4.68x_2 + 0.74x_3 + 1.55x_4 \leq 183$
29. $4.64x_1 + 4.68x_2 + 3.84x_5 \leq 151$
30. $4.64x_1 + 1.55x_4 + 3.84x_5 \leq 96$
31. $4.64x_1 + 0.74x_3 + 1.55x_4 \leq 223$
32. $4.64x_1 + 4.68x_2 + 0.74x_3 \leq 147$
33. $4.68x_2 + 1.55x_4 + 3.84x_5 \leq 167$
34. $4.64x_1 + 4.68x_2 + 0.74x_3 + 1.55x_4 + 3.84x_5 \leq 167$

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'corn cobs'),
        ('x2', 'sashimi'),
        ('x3', 'cornichons'),
        ('x4', 'strips of bacon'),
        ('x5', 'apple pies')
    ],
    'objective_function': '7.63x1 + 8.79x2 + 2.61x3 + 7.13x4 + 2.17x5',
    'constraints': [
        '4.64x1 + 4.68x2 + 0.74x3 + 1.55x4 + 3.84x5 <= 232',
        '0.74x3 + 3.84x5 >= 19',
        '4.68x2 + 3.84x5 >= 26',
        '4.68x2 + 0.74x3 >= 24',
        '4.68x2 + 1.55x4 >= 38',
        '4.64x1 + 3.84x5 >= 15',
        '4.64x1 + 4.68x2 + 0.74x3 >= 40',
        '4.64x1 + 4.68x2 + 3.84x5 >= 40',
        '4.64x1 + 1.55x4 + 3.84x5 >= 40',
        '4.64x1 + 0.74x3 + 1.55x4 >= 40',
        '4.64x1 + 4.68x2 + 0.74x3 >= 38',
        '4.64x1 + 4.68x2 + 3.84x5 >= 38',
        '4.64x1 + 1.55x4 + 3.84x5 >= 38',
        '4.64x1 + 0.74x3 + 1.55x4 >= 38',
        '4.64x1 + 4.68x2 + 0.74x3 >= 33',
        '4.64x1 + 4.68x2 + 3.84x5 >= 33',
        '4.64x1 + 1.55x4 + 3.84x5 >= 33',
        '4.64x1 + 0.74x3 + 1.55x4 >= 33',
        '4.64x1 + 4.68x2 + 0.74x3 >= 26',
        '4.64x1 + 4.68x2 + 3.84x5 >= 26',
        '4.64x1 + 1.55x4 + 3.84x5 >= 26',
        '4.64x1 + 0.74x3 + 1.55x4 >= 26',
        '7x1 - 5x2 + 9x3 >= 0',
        '4.68x2 + 0.74x3 <= 105',
        '4.68x2 + 3.84x5 <= 117',
        '0.74x3 + 1.55x4 <= 206',
        '4.68x2 + 0.74x3 + 3.84x5 <= 85',
        '4.68x2 + 0.74x3 + 1.55x4 <= 183',
        '4.64x1 + 4.68x2 + 3.84x5 <= 151',
        '4.64x1 + 1.55x4 + 3.84x5 <= 96',
        '4.64x1 + 0.74x3 + 1.55x4 <= 223',
        '4.64x1 + 4.68x2 + 0.74x3 <= 147',
        '4.68x2 + 1.55x4 + 3.84x5 <= 167',
        '4.64x1 + 4.68x2 + 0.74x3 + 1.55x4 + 3.84x5 <= 167'
    ]
}
```

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

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

# Define the variables
x1 = model.addVar(name='corn_cobs', vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name='sashimi', vtype=gurobi.GRB.INTEGER)
x3 = model.addVar(name='cornichons', vtype=gurobi.GRB.INTEGER)
x4 = model.addVar(name='strips_of_bacon', vtype=gurobi.GRB.INTEGER)
x5 = model.addVar(name='apple_pies', vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(7.63 * x1 + 8.79 * x2 + 2.61 * x3 + 7.13 * x4 + 2.17 * x5, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 + 1.55 * x4 + 3.84 * x5 <= 232)
model.addConstr(0.74 * x3 + 3.84 * x5 >= 19)
model.addConstr(4.68 * x2 + 3.84 * x5 >= 26)
model.addConstr(4.68 * x2 + 0.74 * x3 >= 24)
model.addConstr(4.68 * x2 + 1.55 * x4 >= 38)
model.addConstr(4.64 * x1 + 3.84 * x5 >= 15)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 >= 40)
model.addConstr(4.64 * x1 + 4.68 * x2 + 3.84 * x5 >= 40)
model.addConstr(4.64 * x1 + 1.55 * x4 + 3.84 * x5 >= 40)
model.addConstr(4.64 * x1 + 0.74 * x3 + 1.55 * x4 >= 40)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 >= 38)
model.addConstr(4.64 * x1 + 4.68 * x2 + 3.84 * x5 >= 38)
model.addConstr(4.64 * x1 + 1.55 * x4 + 3.84 * x5 >= 38)
model.addConstr(4.64 * x1 + 0.74 * x3 + 1.55 * x4 >= 38)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 >= 33)
model.addConstr(4.64 * x1 + 4.68 * x2 + 3.84 * x5 >= 33)
model.addConstr(4.64 * x1 + 1.55 * x4 + 3.84 * x5 >= 33)
model.addConstr(4.64 * x1 + 0.74 * x3 + 1.55 * x4 >= 33)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 >= 26)
model.addConstr(4.64 * x1 + 4.68 * x2 + 3.84 * x5 >= 26)
model.addConstr(4.64 * x1 + 1.55 * x4 + 3.84 * x5 >= 26)
model.addConstr(4.64 * x1 + 0.74 * x3 + 1.55 * x4 >= 26)
model.addConstr(7 * x1 - 5 * x2 + 9 * x3 >= 0)
model.addConstr(4.68 * x2 + 0.74 * x3 <= 105)
model.addConstr(4.68 * x2 + 3.84 * x5 <= 117)
model.addConstr(0.74 * x3 + 1.55 * x4 <= 206)
model.addConstr(4.68 * x2 + 0.74 * x3 + 3.84 * x5 <= 85)
model.addConstr(4.68 * x2 + 0.74 * x3 + 1.55 * x4 <= 183)
model.addConstr(4.64 * x1 + 4.68 * x2 + 3.84 * x5 <= 151)
model.addConstr(4.64 * x1 + 1.55 * x4 + 3.84 * x5 <= 96)
model.addConstr(4.64 * x1 + 0.74 * x3 + 1.55 * x4 <= 223)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 <= 147)
model.addConstr(4.68 * x2 + 1.55 * x4 + 3.84 * x5 <= 167)
model.addConstr(4.64 * x1 + 4.68 * x2 + 0.74 * x3 + 1.55 * x4 + 3.84 * x5 <= 167)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Corn Cobs: ', x1.varValue)
    print('Sashimi: ', x2.varValue)
    print('Cornichons: ', x3.varValue)
    print('Strips of Bacon: ', x4.varValue)
    print('Apple Pies: ', x5.varValue)
else:
    print('No optimal solution found')
```