## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'granola bars' and 'steaks', which we can denote as $x_1$ and $x_2$ respectively. The objective function to minimize is $1 \cdot x_1 + 9 \cdot x_2$. The constraints are:
- $11x_1 + 12x_2 \geq 9$
- $11x_1 + 12x_2 \leq 24$
- $9x_1 - 8x_2 \geq 0$
- $x_1, x_2 \geq 0$ (Implicitly assumed as quantities cannot be negative)

## Step 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for 'granola bars'
- $x_2$ for 'steaks'

The objective function is: $1x_1 + 9x_2$

The constraints are:
- $11x_1 + 12x_2 \geq 9$
- $11x_1 + 12x_2 \leq 24$
- $9x_1 - 8x_2 \geq 0$

## 3: Convert the problem into Gurobi code
To solve this problem using Gurobi, we will use Python. We need to import the Gurobi library, create a model, add variables, set the objective function, and add constraints.

```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="granola_bars", lb=0)  # granola bars
x2 = model.addVar(name="steaks", lb=0)     # steaks

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

# Add constraints
model.addConstr(11 * x1 + 12 * x2 >= 9, name="healthiness_rating_min")
model.addConstr(11 * x1 + 12 * x2 <= 24, name="healthiness_rating_max")
model.addConstr(9 * x1 - 8 * x2 >= 0, name="granola_steak_ratio")

# Optimize the model
model.optimize()

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

## 4: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [('x1', 'granola bars'), ('x2', 'steaks')],
    'objective_function': '1*x1 + 9*x2',
    'constraints': [
        '11*x1 + 12*x2 >= 9',
        '11*x1 + 12*x2 <= 24',
        '9*x1 - 8*x2 >= 0'
    ]
}
```

The final answer is: 
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x1 = model.addVar(name="granola_bars", lb=0)  # granola bars
x2 = model.addVar(name="steaks", lb=0)     # steaks

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

# Add constraints
model.addConstr(11 * x1 + 12 * x2 >= 9, name="healthiness_rating_min")
model.addConstr(11 * x1 + 12 * x2 <= 24, name="healthiness_rating_max")
model.addConstr(9 * x1 - 8 * x2 >= 0, name="granola_steak_ratio")

# Optimize the model
model.optimize()

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