## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'rotisserie chickens' and 'steaks', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $4x_1 + 9x_2$. The constraints are:
- $22x_1 + 14x_2 \geq 77$
- $8x_1 + 4x_2 \geq 75$
- $26x_1 + 20x_2 \geq 43$
- $x_1 - 5x_2 \geq 0$
- $22x_1 + 14x_2 \leq 174$
- $8x_1 + 4x_2 \leq 218$
- $26x_1 + 20x_2 \leq 64$
- $x_1$ is an integer
- $x_2$ is a continuous variable

## Step 2: Convert the problem into a symbolic representation
The symbolic representation of the variables is:
- $x_1$ represents 'rotisserie chickens'
- $x_2$ represents 'steaks'

The objective function is: $4x_1 + 9x_2$

The constraints are:
- $22x_1 + 14x_2 \geq 77$
- $8x_1 + 4x_2 \geq 75$
- $26x_1 + 20x_2 \geq 43$
- $x_1 - 5x_2 \geq 0$
- $22x_1 + 14x_2 \leq 174$
- $8x_1 + 4x_2 \leq 218$
- $26x_1 + 20x_2 \leq 64$

## 3: Provide the symbolic representation in the required format
```json
{
    'sym_variables': [('x1', 'rotisserie chickens'), ('x2', 'steaks')],
    'objective_function': '4*x1 + 9*x2',
    'constraints': [
        '22*x1 + 14*x2 >= 77',
        '8*x1 + 4*x2 >= 75',
        '26*x1 + 20*x2 >= 43',
        'x1 - 5*x2 >= 0',
        '22*x1 + 14*x2 <= 174',
        '8*x1 + 4*x2 <= 218',
        '26*x1 + 20*x2 <= 64'
    ]
}
```

## Step 4: Implement the problem in Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="rotisserie_chickens", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="steaks")

    # Define the objective function
    model.setObjective(4 * x1 + 9 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(22 * x1 + 14 * x2 >= 77)
    model.addConstr(8 * x1 + 4 * x2 >= 75)
    model.addConstr(26 * x1 + 20 * x2 >= 43)
    model.addConstr(x1 - 5 * x2 >= 0)
    model.addConstr(22 * x1 + 14 * x2 <= 174)
    model.addConstr(8 * x1 + 4 * x2 <= 218)
    model.addConstr(26 * x1 + 20 * x2 <= 64)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        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()
```