## Step 1: Define the symbolic representation of the variables
The variables are 'chicken breasts', 'cornichons', and 'slices of pizza', which we can denote as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $1 \times x_0 + 9 \times x_1 + 5 \times x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $6x_0 + 12x_1 + 6x_2 \leq 145$ (milligrams of iron)
- $17x_0 + 12x_1 + 21x_2 \leq 159$ (grams of fiber)
- $12x_1 + 6x_2 \geq 39$ (iron from cornichons and slices of pizza)
- $6x_0 + 12x_1 \geq 43$ (iron from chicken breasts and cornichons)
- $17x_0 + 12x_1 + 21x_2 \geq 30$ (fiber from all sources)
- $6x_0 + 6x_2 \leq 124$ (iron from chicken breasts and slices of pizza)
- $12x_1 + 6x_2 \leq 119$ (iron from cornichons and slices of pizza)
- $6x_0 + 12x_1 + 6x_2 \leq 119$ (iron from all sources)
- $17x_0 + 21x_2 \leq 76$ (fiber from chicken breasts and slices of pizza)
- $17x_0 + 12x_1 \leq 70$ (fiber from chicken breasts and cornichons)
- $17x_0 + 12x_1 + 21x_2 \leq 70$ (fiber from all sources)

## 4: Create a symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'chicken breasts'),
        ('x1', 'cornichons'),
        ('x2', 'slices of pizza')
    ],
    'objective_function': '1*x0 + 9*x1 + 5*x2',
    'constraints': [
        '6*x0 + 12*x1 + 6*x2 <= 145',
        '17*x0 + 12*x1 + 21*x2 <= 159',
        '12*x1 + 6*x2 >= 39',
        '6*x0 + 12*x1 >= 43',
        '17*x0 + 12*x1 + 21*x2 >= 30',
        '6*x0 + 6*x2 <= 124',
        '12*x1 + 6*x2 <= 119',
        '6*x0 + 12*x1 + 6*x2 <= 119',
        '17*x0 + 21*x2 <= 76',
        '17*x0 + 12*x1 <= 70',
        '17*x0 + 12*x1 + 21*x2 <= 70'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="chicken_breasts", lb=0)
    x1 = model.addVar(name="cornichons", lb=0)
    x2 = model.addVar(name="slices_of_pizza", lb=0)

    # Define the objective function
    model.setObjective(1 * x0 + 9 * x1 + 5 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(6 * x0 + 12 * x1 + 6 * x2 <= 145)
    model.addConstr(17 * x0 + 12 * x1 + 21 * x2 <= 159)
    model.addConstr(12 * x1 + 6 * x2 >= 39)
    model.addConstr(6 * x0 + 12 * x1 >= 43)
    model.addConstr(17 * x0 + 12 * x1 + 21 * x2 >= 30)
    model.addConstr(6 * x0 + 6 * x2 <= 124)
    model.addConstr(12 * x1 + 6 * x2 <= 119)
    model.addConstr(6 * x0 + 12 * x1 + 6 * x2 <= 119)
    model.addConstr(17 * x0 + 21 * x2 <= 76)
    model.addConstr(17 * x0 + 12 * x1 <= 70)
    model.addConstr(17 * x0 + 12 * x1 + 21 * x2 <= 70)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("Chicken breasts: ", x0.varValue)
        print("Cornichons: ", x1.varValue)
        print("Slices of pizza: ", x2.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```