## Step 1: Define the symbolic representation of the variables
The variables are: ['bagged salads', 'steaks', 'knishes', 'cherry pies', 'kiwis']. Let's denote them as $x_1, x_2, x_3, x_4, x_5$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $2x_1 + 8x_2 + 5x_3 + 5x_4 + 5x_5$.

## Step 3: List all the constraints in symbolic notation
1. $20x_2 + 11x_3 \geq 19$
2. $5x_1 + 20x_2 \geq 10$
3. $11x_3 + 14x_4 \geq 8$
4. $5x_1 + 14x_4 \geq 13$
5. $5x_1 + 20x_5 \geq 18$
6. $5x_1 + 11x_3 + 14x_4 \geq 14$
7. $5x_1 + 20x_2 + 20x_5 \geq 14$
8. $5x_1 + 11x_3 + 14x_4 \geq 17$
9. $5x_1 + 20x_2 + 20x_5 \geq 17$
10. $5x_1 + 20x_2 + 11x_3 + 14x_4 + 20x_5 \geq 17$
11. $-10x_4 + 9x_5 \geq 0$
12. $8x_3 - 2x_5 \geq 0$
13. $5x_2 - 5x_4 \geq 0$

## 4: Define the symbolic representation of the problem
```json
{
'sym_variables': [
    ('x1', 'bagged salads'), 
    ('x2', 'steaks'), 
    ('x3', 'knishes'), 
    ('x4', 'cherry pies'), 
    ('x5', 'kiwis')
],
'objective_function': '2*x1 + 8*x2 + 5*x3 + 5*x4 + 5*x5',
'constraints': [
    '20*x2 + 11*x3 >= 19',
    '5*x1 + 20*x2 >= 10',
    '11*x3 + 14*x4 >= 8',
    '5*x1 + 14*x4 >= 13',
    '5*x1 + 20*x5 >= 18',
    '5*x1 + 11*x3 + 14*x4 >= 14',
    '5*x1 + 20*x2 + 20*x5 >= 14',
    '5*x1 + 11*x3 + 14*x4 >= 17',
    '5*x1 + 20*x2 + 20*x5 >= 17',
    '5*x1 + 20*x2 + 11*x3 + 14*x4 + 20*x5 >= 17',
    '-10*x4 + 9*x5 >= 0',
    '8*x3 - 2*x5 >= 0',
    '5*x2 - 5*x4 >= 0'
]
}
```

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

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

    # Define the variables
    x1 = model.addVar(name="bagged_salads", lb=0)  # No upper bound
    x2 = model.addVar(name="steaks", lb=0)     # No upper bound
    x3 = model.addVar(name="knishes", lb=0)   # No upper bound
    x4 = model.addVar(name="cherry_pies", lb=0)  # No upper bound
    x5 = model.addVar(name="kiwis", lb=0)     # No upper bound

    # Objective function
    model.setObjective(2*x1 + 8*x2 + 5*x3 + 5*x4 + 5*x5, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(20*x2 + 11*x3 >= 19)
    model.addConstr(5*x1 + 20*x2 >= 10)
    model.addConstr(11*x3 + 14*x4 >= 8)
    model.addConstr(5*x1 + 14*x4 >= 13)
    model.addConstr(5*x1 + 20*x5 >= 18)
    model.addConstr(5*x1 + 11*x3 + 14*x4 >= 14)
    model.addConstr(5*x1 + 20*x2 + 20*x5 >= 14)
    model.addConstr(5*x1 + 11*x3 + 14*x4 >= 17)
    model.addConstr(5*x1 + 20*x2 + 20*x5 >= 17)
    model.addConstr(5*x1 + 20*x2 + 11*x3 + 14*x4 + 20*x5 >= 17)
    model.addConstr(-10*x4 + 9*x5 >= 0)
    model.addConstr(8*x3 - 2*x5 >= 0)
    model.addConstr(5*x2 - 5*x4 >= 0)

    # Add budget constraint
    model.addConstr(5*x1 + 20*x2 + 11*x3 + 14*x4 + 20*x5 <= 131)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("bagged salads: ", x1.varValue)
        print("steaks: ", x2.varValue)
        print("knishes: ", x3.varValue)
        print("cherry pies: ", x4.varValue)
        print("kiwis: ", x5.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```