## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are 'cherry pies', 'tomatoes', 'ravioli', and 'rotisserie chickens', 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 $6.18x_0 + 1.21x_1 + 2.81x_2 + 6.74x_3$.

## 3: List the constraints in symbolic notation
The constraints are:
- $5x_0 + 2x_1 + 8x_2 + 2x_3 \leq 82$ (dollar cost)
- $8x_0 + 8x_1 + 2x_2 + 2x_3 \leq 60$ (grams of carbohydrates)
- $2x_1 + 2x_3 \geq 7$ (minimum spend on tomatoes and rotisserie chickens)
- $5x_0 + 2x_3 \geq 10$ (minimum spend on cherry pies and rotisserie chickens)
- $5x_0 + 8x_2 \geq 18$ (minimum spend on cherry pies and ravioli)
- $5x_0 + 2x_1 \geq 6$ (minimum spend on cherry pies and tomatoes)
- $8x_0 + 2x_2 + 2x_3 \geq 10$ (carbohydrates from cherry pies, ravioli, and rotisserie chickens)
- $8x_0 + 8x_1 + 2x_3 \geq 10$ (carbohydrates from cherry pies, tomatoes, and rotisserie chickens)
- $8x_1 + 2x_2 + 2x_3 \geq 10$ (carbohydrates from tomatoes, ravioli, and rotisserie chickens)
- $8x_0 + 2x_2 + 2x_3 \geq 15$ 
- $8x_0 + 8x_1 + 2x_3 \geq 15$ 
- $8x_1 + 2x_2 + 2x_3 \geq 15$ 
- $5x_0 + 2x_1 \leq 27$ (maximum spend on cherry pies and tomatoes)
- $5x_0 + 2x_3 \leq 51$ (maximum spend on cherry pies and rotisserie chickens)
- $5x_0 + 8x_2 \leq 42$ (maximum spend on cherry pies and ravioli)
- $5x_0 + 2x_1 + 8x_2 + 2x_3 \leq 42$ (maximum total spend)
- $8x_0 + 2x_2 \leq 34$ (carbohydrates from cherry pies and ravioli)
- $2x_2 + 2x_3 \leq 56$ (carbohydrates from ravioli and rotisserie chickens)
- $8x_0 + 8x_1 + 2x_2 + 2x_3 \leq 56$ (total carbohydrates)

## 4: Create the symbolic representation
```json
{
    'sym_variables': [
        ('x0', 'cherry pies'),
        ('x1', 'tomatoes'),
        ('x2', 'ravioli'),
        ('x3', 'rotisserie chickens')
    ],
    'objective_function': '6.18*x0 + 1.21*x1 + 2.81*x2 + 6.74*x3',
    'constraints': [
        '5*x0 + 2*x1 + 8*x2 + 2*x3 <= 82',
        '8*x0 + 8*x1 + 2*x2 + 2*x3 <= 60',
        '2*x1 + 2*x3 >= 7',
        '5*x0 + 2*x3 >= 10',
        '5*x0 + 8*x2 >= 18',
        '5*x0 + 2*x1 >= 6',
        '8*x0 + 2*x2 + 2*x3 >= 10',
        '8*x0 + 8*x1 + 2*x3 >= 10',
        '8*x1 + 2*x2 + 2*x3 >= 10',
        '8*x0 + 2*x2 + 2*x3 >= 15',
        '8*x0 + 8*x1 + 2*x3 >= 15',
        '8*x1 + 2*x2 + 2*x3 >= 15',
        '5*x0 + 2*x1 <= 27',
        '5*x0 + 2*x3 <= 51',
        '5*x0 + 8*x2 <= 42',
        '5*x0 + 2*x1 + 8*x2 + 2*x3 <= 42',
        '8*x0 + 2*x2 <= 34',
        '2*x2 + 2*x3 <= 56',
        '8*x0 + 8*x1 + 2*x2 + 2*x3 <= 56'
    ]
}
```

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

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x0 = model.addVar(name='cherry_pies', lb=0)
    x1 = model.addVar(name='tomatoes', lb=0)
    x2 = model.addVar(name='ravioli', lb=0)
    x3 = model.addVar(name='rotisserie_chickens', lb=0)

    # Define the objective function
    model.setObjective(6.18 * x0 + 1.21 * x1 + 2.81 * x2 + 6.74 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(5 * x0 + 2 * x1 + 8 * x2 + 2 * x3 <= 82)
    model.addConstr(8 * x0 + 8 * x1 + 2 * x2 + 2 * x3 <= 60)
    model.addConstr(2 * x1 + 2 * x3 >= 7)
    model.addConstr(5 * x0 + 2 * x3 >= 10)
    model.addConstr(5 * x0 + 8 * x2 >= 18)
    model.addConstr(5 * x0 + 2 * x1 >= 6)
    model.addConstr(8 * x0 + 2 * x2 + 2 * x3 >= 10)
    model.addConstr(8 * x0 + 8 * x1 + 2 * x3 >= 10)
    model.addConstr(8 * x1 + 2 * x2 + 2 * x3 >= 10)
    model.addConstr(8 * x0 + 2 * x2 + 2 * x3 >= 15)
    model.addConstr(8 * x0 + 8 * x1 + 2 * x3 >= 15)
    model.addConstr(8 * x1 + 2 * x2 + 2 * x3 >= 15)
    model.addConstr(5 * x0 + 2 * x1 <= 27)
    model.addConstr(5 * x0 + 2 * x3 <= 51)
    model.addConstr(5 * x0 + 8 * x2 <= 42)
    model.addConstr(5 * x0 + 2 * x1 + 8 * x2 + 2 * x3 <= 42)
    model.addConstr(8 * x0 + 2 * x2 <= 34)
    model.addConstr(2 * x2 + 2 * x3 <= 56)
    model.addConstr(8 * x0 + 8 * x1 + 2 * x2 + 2 * x3 <= 56)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objVal)
        print('cherry_pies: ', x0.varValue)
        print('tomatoes: ', x1.varValue)
        print('ravioli: ', x2.varValue)
        print('rotisserie_chickens: ', x3.varValue)
    else:
        print('No optimal solution found')

solve_optimization_problem()
```