## Step 1: Define the symbolic representation of the variables
The variables are 'tomatoes', 'apple pies', and 'rotisserie chickens', which we will represent as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to minimize is $2 \times \text{tomatoes} + 3 \times \text{apple pies} + 6 \times \text{rotisserie chickens}$, which in symbolic notation is $2x_0 + 3x_1 + 6x_2$.

## 3: Define the resources/attributes in symbolic notation
The resources/attributes are given as:
- $r_0$: grams of carbohydrates
- $r_1$: tastiness rating
- $r_2$: sourness index
- $r_3$: dollar cost

With their respective values for each item:
- Tomatoes: $r_0 = 17$, $r_1 = 18$, $r_2 = 1$, $r_3 = 18$
- Apple pies: $r_0 = 17$, $r_1 = 11$, $r_2 = 1$, $r_3 = 17$
- Rotisserie chickens: $r_0 = 11$, $r_1 = 10$, $r_2 = 19$, $r_3 = 15$

## 4: Convert the constraints into symbolic notation
The constraints are:
1. $17x_0 + 17x_1 + 11x_2 \geq 26$ (carbohydrates from all sources)
2. $17x_0 + 17x_1 + 11x_2 \geq 26$ is redundant with the previous, but we have $17x_1 + 11x_2 \geq 26$ (carbohydrates from apple pies and rotisserie chickens)
3. $18x_0 + 11x_1 \geq 41$ (tastiness rating from tomatoes and apple pies)
4. $18x_0 + 10x_2 \geq 49$ (tastiness rating from tomatoes and rotisserie chickens)
5. $18x_0 + 11x_1 + 10x_2 \geq 49$ (tastiness rating from all sources)
6. $1x_0 + 1x_1 \geq 17$ (sourness index from tomatoes and apple pies)
7. $1x_0 + 19x_2 \geq 32$ (sourness index from tomatoes and rotisserie chickens)
8. $1x_0 + 1x_1 + 19x_2 \geq 32$ (sourness index from all sources)
9. $18x_0 + 17x_1 \geq 50$ (cost from tomatoes and apple pies)
10. $17x_1 + 15x_2 \geq 67$ (cost from apple pies and rotisserie chickens)
11. $18x_0 + 17x_1 + 15x_2 \geq 67$ (cost from all sources)
12. $-x_0 + 9x_1 \geq 0$
13. $-x_1 + x_2 \geq 0$
14. $17x_1 + 11x_2 \leq 159$ (carbohydrates from apple pies and rotisserie chickens)
15. $17x_0 + 17x_1 + 11x_2 \leq 160$ (carbohydrates from all sources)
16. $18x_0 + 15x_2 \leq 89$ (cost from tomatoes and rotisserie chickens)

## 5: Specify variable bounds
- $x_0$ is an integer (number of tomatoes)
- $x_1$ is continuous (number of apple pies)
- $x_2$ is continuous (number of rotisserie chickens)

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'tomatoes'), ('x1', 'apple pies'), ('x2', 'rotisserie chickens')],
    'objective_function': '2*x0 + 3*x1 + 6*x2',
    'constraints': [
        '17*x1 + 11*x2 >= 26',
        '18*x0 + 11*x1 >= 41',
        '18*x0 + 10*x2 >= 49',
        '18*x0 + 11*x1 + 10*x2 >= 49',
        'x0 + x1 >= 17',
        'x0 + 19*x2 >= 32',
        'x0 + x1 + 19*x2 >= 32',
        '18*x0 + 17*x1 >= 50',
        '17*x1 + 15*x2 >= 67',
        '18*x0 + 17*x1 + 15*x2 >= 67',
        '-x0 + 9*x1 >= 0',
        '-x1 + x2 >= 0',
        '17*x1 + 11*x2 <= 159',
        '17*x0 + 17*x1 + 11*x2 <= 160',
        '18*x0 + 15*x2 <= 89'
    ]
}
```

## 7: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(name="tomatoes", vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name="apple_pies", vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="rotisserie_chickens", vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(2*x0 + 3*x1 + 6*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(17*x1 + 11*x2 >= 26)
    model.addConstr(18*x0 + 11*x1 >= 41)
    model.addConstr(18*x0 + 10*x2 >= 49)
    model.addConstr(18*x0 + 11*x1 + 10*x2 >= 49)
    model.addConstr(x0 + x1 >= 17)
    model.addConstr(x0 + 19*x2 >= 32)
    model.addConstr(x0 + x1 + 19*x2 >= 32)
    model.addConstr(18*x0 + 17*x1 >= 50)
    model.addConstr(17*x1 + 15*x2 >= 67)
    model.addConstr(18*x0 + 17*x1 + 15*x2 >= 67)
    model.addConstr(-x0 + 9*x1 >= 0)
    model.addConstr(-x1 + x2 >= 0)
    model.addConstr(17*x1 + 11*x2 <= 159)
    model.addConstr(17*x0 + 17*x1 + 11*x2 <= 160)
    model.addConstr(18*x0 + 15*x2 <= 89)

    # Optimize
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Tomatoes: {x0.varValue}")
        print(f"Apple pies: {x1.varValue}")
        print(f"Rotisserie chickens: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```