To tackle the given optimization problem, we first need to break down the components into a symbolic representation. The variables are 'rotisserie chickens' and 'apple pies', which we'll denote as $x_0$ and $x_1$, respectively.

### Symbolic Representation:

- **Variables:** 
  - $x_0$: rotisserie chickens
  - $x_1$: apple pies

- **Objective Function:**
  The goal is to maximize the quantity of rotisserie chickens plus the quantity of apple pies, which can be represented as:
  \[
  \text{Maximize: } x_0 + x_1
  \]

- **Constraints:**
  1. Fat from rotisserie chickens and apple pies must total at least 41 grams.
     - $12x_0 + x_1 \geq 41$
  2. Carbohydrates from both must total at least 20 grams.
     - $14x_0 + 15x_1 \geq 20$
  3. The combination of ten times the number of rotisserie chickens and minus eight times the number of apple pies must be greater than or equal to zero.
     - $10x_0 - 8x_1 \geq 0$
  4. Total fat from both sources must not exceed 48 grams.
     - $12x_0 + x_1 \leq 48$
  5. Total carbohydrates from both must not exceed 35 grams.
     - $14x_0 + 15x_1 \leq 35$

### Gurobi Code:

Given the symbolic representation, we can now formulate this problem in Gurobi. The code below sets up and solves the optimization problem.

```python
from gurobipy import *

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

# Define variables (allowing for fractional amounts)
x0 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="rotisserie_chickens")
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="apple_pies")

# Objective function: Maximize the quantity of both items
m.setObjective(x0 + x1, GRB.MAXIMIZE)

# Constraints
m.addConstr(12*x0 + x1 >= 41, name="fat_minimum")
m.addConstr(14*x0 + 15*x1 >= 20, name="carbohydrates_minimum")
m.addConstr(10*x0 - 8*x1 >= 0, name="combination_constraint")
m.addConstr(12*x0 + x1 <= 48, name="fat_maximum")
m.addConstr(14*x0 + 15*x1 <= 35, name="carbohydrates_maximum")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Rotisserie Chickens: {x0.x}")
    print(f"Apple Pies: {x1.x}")
else:
    print("No optimal solution found")

```

This code sets up the model, defines the variables and constraints based on the problem description, and solves for the maximum quantity of rotisserie chickens and apple pies under the given conditions. 

### Symbolic Representation in JSON Format

```json
{
  "sym_variables": [
    ("x0", "rotisserie chickens"),
    ("x1", "apple pies")
  ],
  "objective_function": "Maximize: x0 + x1",
  "constraints": [
    "12*x0 + x1 >= 41",
    "14*x0 + 15*x1 >= 20",
    "10*x0 - 8*x1 >= 0",
    "12*x0 + x1 <= 48",
    "14*x0 + 15*x1 <= 35"
  ]
}
```