## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'kiwis', 'hamburgers', 'strips of bacon', and 'chicken breasts', which we can denote as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $4.85 \times \text{kiwis} + 5.61 \times \text{hamburgers} + 8.82 \times \text{strips of bacon} + 6.75 \times \text{chicken breasts}$. In symbolic notation, this becomes $4.85x_0 + 5.61x_1 + 8.82x_2 + 6.75x_3$.

## 3: List the constraints in symbolic notation
The constraints given are:
- $8x_0 \leq 123$
- $8x_1 \leq 123$
- $9x_2 \leq 123$
- $5x_3 \leq 123$
- $2x_0 + 7x_1 + 5x_2 + x_3 \leq 155$
- $8x_1 + 5x_3 \geq 26$
- $8x_0 + 9x_2 \geq 27$
- $8x_1 + 9x_2 \geq 25$
- $8x_0 + 8x_1 \geq 14$
- $5x_2 + x_3 \geq 36$
- $7x_1 + 5x_2 \geq 34$
- $2x_0 + 5x_2 + x_3 \geq 34$
- $7x_1 + 5x_2 + x_3 \geq 34$
- $2x_0 + 7x_1 + 5x_2 \geq 34$
- $2x_0 + 5x_2 + x_3 \geq 26$
- $7x_1 + 5x_2 + x_3 \geq 26$
- $2x_0 + 7x_1 + 5x_2 \geq 26$
- $2x_0 + 5x_2 + x_3 \geq 19$
- $7x_1 + 5x_2 + x_3 \geq 19$
- $2x_0 + 7x_1 + 5x_2 \geq 19$
- $8x_0 + 9x_2 \leq 74$
- $8x_0 + 5x_3 \leq 104$
- $8x_1 + 9x_2 + 5x_3 \leq 93$
- $8x_0 + 8x_1 + 9x_2 + 5x_3 \leq 93$
- $7x_1 + 5x_2 \leq 113$
- $2x_0 + 7x_1 + x_3 \leq 93$
- $2x_0 + 5x_2 + x_3 \leq 46$
- $2x_0 + 7x_1 + 5x_2 + x_3 \leq 46$

## 4: Define the symbolic variables and constraints for the JSON output
The symbolic variables are:
- $x_0$ for 'kiwis'
- $x_1$ for 'hamburgers'
- $x_2$ for 'strips of bacon'
- $x_3$ for 'chicken breasts'

## 5: Prepare the JSON output
```json
{
    'sym_variables': [('x0', 'kiwis'), ('x1', 'hamburgers'), ('x2', 'strips of bacon'), ('x3', 'chicken breasts')],
    'objective_function': '4.85x0 + 5.61x1 + 8.82x2 + 6.75x3',
    'constraints': [
        '8x0 <= 123',
        '8x1 <= 123',
        '9x2 <= 123',
        '5x3 <= 123',
        '2x0 + 7x1 + 5x2 + x3 <= 155',
        '8x1 + 5x3 >= 26',
        '8x0 + 9x2 >= 27',
        '8x1 + 9x2 >= 25',
        '8x0 + 8x1 >= 14',
        '5x2 + x3 >= 36',
        '7x1 + 5x2 >= 34',
        '2x0 + 5x2 + x3 >= 34',
        '7x1 + 5x2 + x3 >= 34',
        '2x0 + 7x1 + 5x2 >= 34',
        '2x0 + 5x2 + x3 >= 26',
        '7x1 + 5x2 + x3 >= 26',
        '2x0 + 7x1 + 5x2 >= 26',
        '2x0 + 5x2 + x3 >= 19',
        '7x1 + 5x2 + x3 >= 19',
        '2x0 + 7x1 + 5x2 >= 19',
        '8x0 + 9x2 <= 74',
        '8x0 + 5x3 <= 104',
        '8x1 + 9x2 + 5x3 <= 93',
        '8x0 + 8x1 + 9x2 + 5x3 <= 93',
        '7x1 + 5x2 <= 113',
        '2x0 + 7x1 + x3 <= 93',
        '2x0 + 5x2 + x3 <= 46',
        '2x0 + 7x1 + 5x2 + x3 <= 46'
    ]
}
```

## 6: Write the Gurobi code
```python
import gurobi

# Define the model
m = gurobi.Model()

# Define the variables
x0 = m.addVar(name="kiwis", lb=0, ub=None)
x1 = m.addVar(name="hamburgers", lb=0, ub=None, integrality=1)
x2 = m.addVar(name="strips of bacon", lb=0, ub=None)
x3 = m.addVar(name="chicken breasts", lb=0, ub=None, integrality=1)

# Define the objective function
m.setObjective(4.85 * x0 + 5.61 * x1 + 8.82 * x2 + 6.75 * x3, gurobi.GRB.MAXIMIZE)

# Add constraints
m.addConstr(8 * x0 <= 123)
m.addConstr(8 * x1 <= 123)
m.addConstr(9 * x2 <= 123)
m.addConstr(5 * x3 <= 123)
m.addConstr(2 * x0 + 7 * x1 + 5 * x2 + x3 <= 155)
m.addConstr(8 * x1 + 5 * x3 >= 26)
m.addConstr(8 * x0 + 9 * x2 >= 27)
m.addConstr(8 * x1 + 9 * x2 >= 25)
m.addConstr(8 * x0 + 8 * x1 >= 14)
m.addConstr(5 * x2 + x3 >= 36)
m.addConstr(7 * x1 + 5 * x2 >= 34)
m.addConstr(2 * x0 + 5 * x2 + x3 >= 34)
m.addConstr(7 * x1 + 5 * x2 + x3 >= 34)
m.addConstr(2 * x0 + 7 * x1 + 5 * x2 >= 34)
m.addConstr(2 * x0 + 5 * x2 + x3 >= 26)
m.addConstr(7 * x1 + 5 * x2 + x3 >= 26)
m.addConstr(2 * x0 + 7 * x1 + 5 * x2 >= 26)
m.addConstr(2 * x0 + 5 * x2 + x3 >= 19)
m.addConstr(7 * x1 + 5 * x2 + x3 >= 19)
m.addConstr(2 * x0 + 7 * x1 + 5 * x2 >= 19)
m.addConstr(8 * x0 + 9 * x2 <= 74)
m.addConstr(8 * x0 + 5 * x3 <= 104)
m.addConstr(8 * x1 + 9 * x2 + 5 * x3 <= 93)
m.addConstr(8 * x0 + 8 * x1 + 9 * x2 + 5 * x3 <= 93)
m.addConstr(7 * x1 + 5 * x2 <= 113)
m.addConstr(2 * x0 + 7 * x1 + x3 <= 93)
m.addConstr(2 * x0 + 5 * x2 + x3 <= 46)
m.addConstr(2 * x0 + 7 * x1 + 5 * x2 + x3 <= 46)

# Optimize the model
m.optimize()

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