## Step 1: Define the symbolic representation of the variables
The variables are 'apple pies', 'sashimi', and 'fruit salads', which we will denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : 'apple pies'
- $x_1$ : 'sashimi'
- $x_2$ : 'fruit salads'

## 3: Define the objective function in symbolic notation
The objective function to maximize is $6.65x_0 + 8.92x_1 + 8.8x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $12x_0 + 10x_1 + 12x_2 \leq 116$ (milligrams of iron)
- $9x_0 + 3x_1 + 14x_2 \leq 247$ (grams of carbohydrates)
- $11x_0 + 2x_1 + 10x_2 \leq 264$ (tastiness rating)
- $2x_0 + 10x_1 + x_2 \leq 112$ (dollar cost)
- $12x_0 + 12x_2 \geq 23$ (milligrams of iron from apple pies and fruit salads)
- $10x_1 + x_2 \geq 26$ (dollar cost from sashimi and fruit salads)
- $12x_0 + 10x_1 \leq 83$ (milligrams of iron from apple pies and sashimi)
- $12x_0 + 12x_2 \leq 47$ (milligrams of iron from apple pies and fruit salads)
- $12x_0 + 10x_1 + 12x_2 \leq 47$ (milligrams of iron from all)
- $9x_0 + 3x_1 + 14x_2 \leq 164$ (grams of carbohydrates from apple pies and sashimi)
- $9x_0 + 3x_1 + 14x_2 \leq 143$ (grams of carbohydrates from all, noting the upper bound is effectively 143)
- $11x_0 + 2x_1 \leq 215$ (tastiness rating from apple pies and sashimi)
- $2x_1 + 10x_2 \leq 88$ (tastiness rating from sashimi and fruit salads)
- $11x_0 + 2x_1 + 10x_2 \leq 100$ (tastiness rating from all)
- $2x_0 + x_2 \leq 70$ (dollar cost from apple pies and fruit salads)
- $2x_0 + 10x_1 + x_2 \leq 44$ (dollar cost from all)

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x0', 'apple pies'), ('x1', 'sashimi'), ('x2', 'fruit salads')],
    'objective_function': '6.65*x0 + 8.92*x1 + 8.8*x2',
    'constraints': [
        '12*x0 + 10*x1 + 12*x2 <= 116',
        '9*x0 + 3*x1 + 14*x2 <= 247',
        '11*x0 + 2*x1 + 10*x2 <= 264',
        '2*x0 + 10*x1 + x2 <= 112',
        '12*x0 + 12*x2 >= 23',
        '10*x1 + x2 >= 26',
        '12*x0 + 10*x1 <= 83',
        '12*x0 + 12*x2 <= 47',
        '12*x0 + 10*x1 + 12*x2 <= 47',
        '9*x0 + 3*x1 <= 164',
        '9*x0 + 3*x1 + 14*x2 <= 143',
        '11*x0 + 2*x1 <= 215',
        '2*x1 + 10*x2 <= 88',
        '11*x0 + 2*x1 + 10*x2 <= 100',
        '2*x0 + x2 <= 70',
        '2*x0 + 10*x1 + x2 <= 44'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="apple_pies", lb=0)
    x1 = model.addVar(name="sashimi", lb=0)
    x2 = model.addVar(name="fruit_salads", lb=0)

    # Define the objective function
    model.setObjective(6.65 * x0 + 8.92 * x1 + 8.8 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(12 * x0 + 10 * x1 + 12 * x2 <= 116)  # milligrams of iron
    model.addConstr(9 * x0 + 3 * x1 + 14 * x2 <= 247)  # grams of carbohydrates
    model.addConstr(11 * x0 + 2 * x1 + 10 * x2 <= 264)  # tastiness rating
    model.addConstr(2 * x0 + 10 * x1 + x2 <= 112)  # dollar cost
    model.addConstr(12 * x0 + 12 * x2 >= 23)  # milligrams of iron from apple pies and fruit salads
    model.addConstr(10 * x1 + x2 >= 26)  # dollar cost from sashimi and fruit salads
    model.addConstr(12 * x0 + 10 * x1 <= 83)  # milligrams of iron from apple pies and sashimi
    model.addConstr(12 * x0 + 12 * x2 <= 47)  # milligrams of iron from apple pies and fruit salads
    model.addConstr(12 * x0 + 10 * x1 + 12 * x2 <= 47)  # milligrams of iron from all
    model.addConstr(9 * x0 + 3 * x1 <= 164)  # grams of carbohydrates from apple pies and sashimi
    model.addConstr(9 * x0 + 3 * x1 + 14 * x2 <= 143)  # grams of carbohydrates from all
    model.addConstr(11 * x0 + 2 * x1 <= 215)  # tastiness rating from apple pies and sashimi
    model.addConstr(2 * x1 + 10 * x2 <= 88)  # tastiness rating from sashimi and fruit salads
    model.addConstr(11 * x0 + 2 * x1 + 10 * x2 <= 100)  # tastiness rating from all
    model.addConstr(2 * x0 + x2 <= 70)  # dollar cost from apple pies and fruit salads
    model.addConstr(2 * x0 + 10 * x1 + x2 <= 44)  # dollar cost from all

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Apple Pies: ", x0.varValue)
        print("Sashimi: ", x1.varValue)
        print("Fruit Salads: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```