To solve the given optimization problem, we first need to understand and translate the natural language description into a symbolic representation. This involves identifying the variables, the objective function, and the constraints.

### Symbolic Representation

- **Variables**: Let $x_0$ represent the quantity of rotisserie chickens and $x_1$ represent the number of apples.
- **Objective Function**: The goal is to minimize $8.8x_0 + 7.52x_1$.
- **Constraints**:
  1. Fat constraint: $x_0 + 12x_1 \geq 11$
  2. Cost constraint (minimum): $4x_0 + 10x_1 \geq 11$
  3. Cost constraint (maximum): $4x_0 + 10x_1 \leq 55$
  4. Additional constraint: $10x_0 - 6x_1 \geq 0$
  5. Fat upper bound: $x_0 + 12x_1 \leq 43$
  6. Non-negativity and integer constraints: $x_0 \geq 0$, $x_1 \geq 0$, and $x_0$ must be an integer.

### Gurobi Code

To implement this problem in Gurobi, we will use Python as the programming language. First, ensure you have Gurobi installed in your Python environment. You can install it via `pip install gurobipy`.

```python
from gurobipy import *

# Create a model
m = Model("Optimization_Problem")

# Define variables
x0 = m.addVar(vtype=GRB.INTEGER, name="rotisserie_chickens")
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="apples")

# Set objective function
m.setObjective(8.8*x0 + 7.52*x1, GRB.MINIMIZE)

# Add constraints
m.addConstr(x0 + 12*x1 >= 11, "fat_minimum")
m.addConstr(4*x0 + 10*x1 >= 11, "cost_minimum")
m.addConstr(4*x0 + 10*x1 <= 55, "cost_maximum")
m.addConstr(10*x0 - 6*x1 >= 0, "additional_constraint")
m.addConstr(x0 + 12*x1 <= 43, "fat_upper_bound")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rotisserie chickens: {x0.x}")
    print(f"Apples: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```

### Symbolic Representation in JSON Format

```json
{
  "sym_variables": [
    ("x0", "rotisserie chickens"),
    ("x1", "apples")
  ],
  "objective_function": "8.8*x0 + 7.52*x1",
  "constraints": [
    "x0 + 12*x1 >= 11",
    "4*x0 + 10*x1 >= 11",
    "4*x0 + 10*x1 <= 55",
    "10*x0 - 6*x1 >= 0",
    "x0 + 12*x1 <= 43"
  ]
}
```