## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'oranges', 'bananas', 'peanutbutter sandwiches', 'pickles', and 'cornichons', which can be represented symbolically as $x_1, x_2, x_3, x_4, x_5$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $8x_1 + 6x_2 + 5x_3 + 3x_4 + 6x_5$.

## 3: List the constraints in symbolic notation
The constraints given are:
1. $2x_3 + 14x_5 \geq 17$
2. $11x_1 + 14x_5 \geq 16$
3. $9x_2 + 2x_3 \geq 18$
4. $2x_3 + 17x_4 \geq 30$
5. $x_3 + 17x_4 + 14x_5 \geq 24$
6. $11x_1 + 17x_4 + 14x_5 \geq 24$
7. $9x_2 + 2x_3 + 14x_5 \geq 24$
8. $9x_2 + 2x_3 + 17x_4 \geq 24$
9. $2x_3 + 17x_4 + 14x_5 \geq 27$
10. $11x_1 + 17x_4 + 14x_5 \geq 27$
11. $9x_2 + 2x_3 + 14x_5 \geq 27$
12. $9x_2 + 2x_3 + 17x_4 \geq 27$
13. $2x_3 + 17x_4 + 14x_5 \geq 35$
14. $11x_1 + 17x_4 + 14x_5 \geq 35$
15. $9x_2 + 2x_3 + 14x_5 \geq 35$
16. $9x_2 + 2x_3 + 17x_4 \geq 35$
17. $2x_3 + 17x_4 + 14x_5 \geq 27$
18. $11x_1 + 17x_4 + 14x_5 \geq 27$
19. $9x_2 + 2x_3 + 14x_5 \geq 27$
20. $9x_2 + 2x_3 + 17x_4 \geq 27$
21. $11x_1 + 14x_5 \leq 55$
22. $2x_3 + 14x_5 \leq 158$
23. $11x_1 + 17x_4 \leq 154$
24. $17x_4 + 14x_5 \leq 122$
25. $11x_1 + 2x_3 \leq 204$
26. $2x_3 + 17x_4 \leq 119$
27. $11x_1 + 2x_3 + 17x_4 \leq 85$
28. $11x_1 + 9x_2 + 2x_3 + 17x_4 + 14x_5 \leq 85$

## 4: Define the symbolic variables and constraints for Gurobi
The symbolic representation is:
```json
{
'sym_variables': [
    ('x1', 'oranges'), 
    ('x2', 'bananas'), 
    ('x3', 'peanutbutter sandwiches'), 
    ('x4', 'pickles'), 
    ('x5', 'cornichons')
],
'objective_function': '8*x1 + 6*x2 + 5*x3 + 3*x4 + 6*x5',
'constraints': [
    '2*x3 + 14*x5 >= 17',
    '11*x1 + 14*x5 >= 16',
    '9*x2 + 2*x3 >= 18',
    '2*x3 + 17*x4 >= 30',
    'x3 + 17*x4 + 14*x5 >= 24',
    '11*x1 + 17*x4 + 14*x5 >= 24',
    '9*x2 + 2*x3 + 14*x5 >= 24',
    '9*x2 + 2*x3 + 17*x4 >= 24',
    '2*x3 + 17*x4 + 14*x5 >= 27',
    '11*x1 + 17*x4 + 14*x5 >= 27',
    '9*x2 + 2*x3 + 14*x5 >= 27',
    '9*x2 + 2*x3 + 17*x4 >= 27',
    '2*x3 + 17*x4 + 14*x5 >= 35',
    '11*x1 + 17*x4 + 14*x5 >= 35',
    '9*x2 + 2*x3 + 14*x5 >= 35',
    '9*x2 + 2*x3 + 17*x4 >= 35',
    '2*x3 + 17*x4 + 14*x5 >= 27',
    '11*x1 + 17*x4 + 14*x5 >= 27',
    '9*x2 + 2*x3 + 14*x5 >= 27',
    '9*x2 + 2*x3 + 17*x4 >= 27',
    '11*x1 + 14*x5 <= 55',
    '2*x3 + 14*x5 <= 158',
    '11*x1 + 17*x4 <= 154',
    '17*x4 + 14*x5 <= 122',
    '11*x1 + 2*x3 <= 204',
    '2*x3 + 17*x4 <= 119',
    '11*x1 + 2*x3 + 17*x4 <= 85',
    '11*x1 + 9*x2 + 2*x3 + 17*x4 + 14*x5 <= 85'
]
}
```

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

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

# Define the variables
oranges = m.addVar(name='oranges', lb=0)
bananas = m.addVar(name='bananas', lb=0)
peanutbutter_sandwiches = m.addVar(name='peanutbutter_sandwiches', lb=0)
pickles = m.addVar(name='pickles', lb=0)
cornichons = m.addVar(name='cornichons', lb=0)

# Objective function
m.setObjective(8*oranges + 6*bananas + 5*peanutbutter_sandwiches + 3*pickles + 6*cornichons, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(2*peanutbutter_sandwiches + 14*cornichons >= 17)
m.addConstr(11*oranges + 14*cornichons >= 16)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches >= 18)
m.addConstr(2*peanutbutter_sandwiches + 17*pickles >= 30)
m.addConstr(peanutbutter_sandwiches + 17*pickles + 14*cornichons >= 24)
m.addConstr(11*oranges + 17*pickles + 14*cornichons >= 24)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 14*cornichons >= 24)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 17*pickles >= 24)
m.addConstr(2*peanutbutter_sandwiches + 17*pickles + 14*cornichons >= 27)
m.addConstr(11*oranges + 17*pickles + 14*cornichons >= 27)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 14*cornichons >= 27)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 17*pickles >= 27)
m.addConstr(2*peanutbutter_sandwiches + 17*pickles + 14*cornichons >= 35)
m.addConstr(11*oranges + 17*pickles + 14*cornichons >= 35)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 14*cornichons >= 35)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 17*pickles >= 35)
m.addConstr(2*peanutbutter_sandwiches + 17*pickles + 14*cornichons >= 27)
m.addConstr(11*oranges + 17*pickles + 14*cornichons >= 27)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 14*cornichons >= 27)
m.addConstr(9*bananas + 2*peanutbutter_sandwiches + 17*pickles >= 27)
m.addConstr(11*oranges + 14*cornichons <= 55)
m.addConstr(2*peanutbutter_sandwiches + 14*cornichons <= 158)
m.addConstr(11*oranges + 17*pickles <= 154)
m.addConstr(17*pickles + 14*cornichons <= 122)
m.addConstr(11*oranges + 2*peanutbutter_sandwiches <= 204)
m.addConstr(2*peanutbutter_sandwiches + 17*pickles <= 119)
m.addConstr(11*oranges + 2*peanutbutter_sandwiches + 17*pickles <= 85)
m.addConstr(11*oranges + 9*bananas + 2*peanutbutter_sandwiches + 17*pickles + 14*cornichons <= 85)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('Oranges: ', oranges.varValue)
    print('Bananas: ', bananas.varValue)
    print('Peanutbutter sandwiches: ', peanutbutter_sandwiches.varValue)
    print('Pickles: ', pickles.varValue)
    print('Cornichons: ', cornichons.varValue)
else:
    print('The model is infeasible')
```