## Step 1: Define the symbolic representation of the variables
The variables are: ['chicken breasts', 'potatoes', 'rotisserie chickens', 'bagged salads', 'bananas']. 
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: $6.84x_1 + 8.92x_2 + 2.94x_3 + 8.74x_4 + 7.66x_5$.

## Step 3: List all the constraints in symbolic notation
1. $14x_3 + 2x_4 \geq 36$
2. $9x_2 + 2x_4 \geq 39$
3. $14x_3 + 2x_4 + 13x_5 \geq 26$
4. $21x_1 + 9x_2 + 14x_3 \geq 26$
5. $21x_1 + 9x_2 + 13x_5 \geq 26$
6. $14x_3 + 2x_4 + 13x_5 \geq 44$
7. $21x_1 + 9x_2 + 14x_3 \geq 44$
8. $21x_1 + 9x_2 + 13x_5 \geq 44$
9. $14x_3 + 2x_4 + 13x_5 \geq 42$
10. $21x_1 + 9x_2 + 14x_3 \geq 42$
11. $21x_1 + 9x_2 + 13x_5 \geq 42$
12. $21x_1 + 9x_2 + 14x_3 + 2x_4 + 13x_5 \geq 42$
13. $-10x_2 + 2x_5 \geq 0$
14. $10x_2 - 10x_3 \geq 0$
15. $21x_1 + 2x_4 + 13x_5 \leq 68$
16. $14x_3 + 2x_4 + 13x_5 \leq 168$
17. $21x_1 + 14x_3 + 13x_5 \leq 85$
18. $21x_1 + 9x_2 + 13x_5 \leq 160$
19. $21x_1 + 14x_3 + 2x_4 \leq 156$
20. $9x_2 + 14x_3 + 13x_5 \leq 126$
21. $9x_2 + 2x_4 + 13x_5 \leq 78$
22. $21x_1 + 9x_2 + 14x_3 \leq 154$

## Step 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'chicken breasts'), 
        ('x2', 'potatoes'), 
        ('x3', 'rotisserie chickens'), 
        ('x4', 'bagged salads'), 
        ('x5', 'bananas')
    ], 
    'objective_function': '6.84*x1 + 8.92*x2 + 2.94*x3 + 8.74*x4 + 7.66*x5', 
    'constraints': [
        '14*x3 + 2*x4 >= 36',
        '9*x2 + 2*x4 >= 39',
        '14*x3 + 2*x4 + 13*x5 >= 26',
        '21*x1 + 9*x2 + 14*x3 >= 26',
        '21*x1 + 9*x2 + 13*x5 >= 26',
        '14*x3 + 2*x4 + 13*x5 >= 44',
        '21*x1 + 9*x2 + 14*x3 >= 44',
        '21*x1 + 9*x2 + 13*x5 >= 44',
        '14*x3 + 2*x4 + 13*x5 >= 42',
        '21*x1 + 9*x2 + 14*x3 >= 42',
        '21*x1 + 9*x2 + 13*x5 >= 42',
        '21*x1 + 9*x2 + 14*x3 + 2*x4 + 13*x5 >= 42',
        '-10*x2 + 2*x5 >= 0',
        '10*x2 - 10*x3 >= 0',
        '21*x1 + 2*x4 + 13*x5 <= 68',
        '14*x3 + 2*x4 + 13*x5 <= 168',
        '21*x1 + 14*x3 + 13*x5 <= 85',
        '21*x1 + 9*x2 + 13*x5 <= 160',
        '21*x1 + 14*x3 + 2*x4 <= 156',
        '9*x2 + 14*x3 + 13*x5 <= 126',
        '9*x2 + 2*x4 + 13*x5 <= 78',
        '21*x1 + 9*x2 + 14*x3 <= 154'
    ]
}
```

## Step 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

# Define the variables
x1 = model.addVar(name="chicken breasts", lb=0)
x2 = model.addVar(name="potatoes", lb=0)
x3 = model.addVar(name="rotisserie chickens", lb=0)
x4 = model.addVar(name="bagged salads", lb=0)
x5 = model.addVar(name="bananas", lb=0)

# Define the objective function
model.setObjective(6.84 * x1 + 8.92 * x2 + 2.94 * x3 + 8.74 * x4 + 7.66 * x5, gurobi.GRB.MINIMIZE)

# Add constraints
model.addConstr(14 * x3 + 2 * x4 >= 36)
model.addConstr(9 * x2 + 2 * x4 >= 39)
model.addConstr(14 * x3 + 2 * x4 + 13 * x5 >= 26)
model.addConstr(21 * x1 + 9 * x2 + 14 * x3 >= 26)
model.addConstr(21 * x1 + 9 * x2 + 13 * x5 >= 26)
model.addConstr(14 * x3 + 2 * x4 + 13 * x5 >= 44)
model.addConstr(21 * x1 + 9 * x2 + 14 * x3 >= 44)
model.addConstr(21 * x1 + 9 * x2 + 13 * x5 >= 44)
model.addConstr(14 * x3 + 2 * x4 + 13 * x5 >= 42)
model.addConstr(21 * x1 + 9 * x2 + 14 * x3 >= 42)
model.addConstr(21 * x1 + 9 * x2 + 13 * x5 >= 42)
model.addConstr(21 * x1 + 9 * x2 + 14 * x3 + 2 * x4 + 13 * x5 >= 42)
model.addConstr(-10 * x2 + 2 * x5 >= 0)
model.addConstr(10 * x2 - 10 * x3 >= 0)
model.addConstr(21 * x1 + 2 * x4 + 13 * x5 <= 68)
model.addConstr(14 * x3 + 2 * x4 + 13 * x5 <= 168)
model.addConstr(21 * x1 + 14 * x3 + 13 * x5 <= 85)
model.addConstr(21 * x1 + 9 * x2 + 13 * x5 <= 160)
model.addConstr(21 * x1 + 14 * x3 + 2 * x4 <= 156)
model.addConstr(9 * x2 + 14 * x3 + 13 * x5 <= 126)
model.addConstr(9 * x2 + 2 * x4 + 13 * x5 <= 78)
model.addConstr(21 * x1 + 9 * x2 + 14 * x3 <= 154)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Objective: ", model.objVal)
    print("Chicken breasts: ", x1.varValue)
    print("Potatoes: ", x2.varValue)
    print("Rotisserie chickens: ", x3.varValue)
    print("Bagged salads: ", x4.varValue)
    print("Bananas: ", x5.varValue)
else:
    print("The model is infeasible")
```