## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. Let's define the symbolic variables:
- $x_0$ represents the number of blueberry pies
- $x_1$ represents the number of rotisserie chickens
- $x_2$ represents the number of steaks

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $4x_0 + 6x_1 + 6x_2$.

## 3: List all constraints in symbolic notation
1. $19x_0 + 4x_1 + 32x_2 \geq 72$ (minimum spend)
2. $20x_0 + 24x_1 + 34x_2 \geq 52$ (minimum protein)
3. $19x_0 + 32x_2 \leq 151$ (max spend on blueberry pies and steaks)
4. $19x_0 + 4x_1 \leq 162$ (max spend on blueberry pies and rotisserie chickens)
5. $4x_1 + 32x_2 \leq 190$ (max spend on rotisserie chickens and steaks)
6. $19x_0 + 4x_1 + 32x_2 \leq 196$ (total max spend)
7. $24x_1 + 34x_2 \leq 61$ (max protein from rotisserie chickens and steaks)
8. $20x_0 + 24x_1 \leq 78$ (max protein from blueberry pies and rotisserie chickens)
9. $20x_0 + 24x_1 + 34x_2 \leq 162$ (max total protein)

## 4: Define the symbolic representation of the problem in JSON format
```json
{
    'sym_variables': [
        ('x0', 'blueberry pies'),
        ('x1', 'rotisserie chickens'),
        ('x2', 'steaks')
    ],
    'objective_function': '4*x0 + 6*x1 + 6*x2',
    'constraints': [
        '19*x0 + 4*x1 + 32*x2 >= 72',
        '20*x0 + 24*x1 + 34*x2 >= 52',
        '19*x0 + 32*x2 <= 151',
        '19*x0 + 4*x1 <= 162',
        '4*x1 + 32*x2 <= 190',
        '19*x0 + 4*x1 + 32*x2 <= 196',
        '24*x1 + 34*x2 <= 61',
        '20*x0 + 24*x1 <= 78',
        '20*x0 + 24*x1 + 34*x2 <= 162'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define variables
    x0 = model.addVar(name="blueberry_pies", lb=0, ub=None, type=gurobi.GRB.CONTINUOUS)
    x1 = model.addVar(name="rotisserie_chickens", lb=0, ub=None, type=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="steaks", lb=0, ub=None, type=gurobi.GRB.INTEGER)

    # Define objective function
    model.setObjective(4 * x0 + 6 * x1 + 6 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(19 * x0 + 4 * x1 + 32 * x2 >= 72)
    model.addConstr(20 * x0 + 24 * x1 + 34 * x2 >= 52)
    model.addConstr(19 * x0 + 32 * x2 <= 151)
    model.addConstr(19 * x0 + 4 * x1 <= 162)
    model.addConstr(4 * x1 + 32 * x2 <= 190)
    model.addConstr(19 * x0 + 4 * x1 + 32 * x2 <= 196)
    model.addConstr(24 * x1 + 34 * x2 <= 61)
    model.addConstr(20 * x0 + 24 * x1 <= 78)
    model.addConstr(20 * x0 + 24 * x1 + 34 * x2 <= 162)

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```