To convert the given natural language description into a symbolic representation and then into Gurobi code, we first identify the variables and their corresponding natural language objects. We have two main items: 'granola bars' and 'steaks'. Let's denote the number of granola bars as \(x_1\) and the number of steaks as \(x_2\).

The objective function is to minimize \(1 \times x_1 + 9 \times x_2\), which can be written symbolically as:
\[ \text{Minimize: } x_1 + 9x_2 \]

Given constraints are:
1. The total combined healthiness rating from granola bars and steaks has to be 9 or more.
2. This constraint is repeated, so we consider it only once.
3. \(9x_1 - 8x_2 \geq 0\)
4. The total combined healthiness rating from granola bars and steaks should be 24 at maximum.

Let's represent the constraints symbolically:
- Healthiness rating constraint (minimum): \(11x_1 + 12x_2 \geq 9\)
- Constraint from the third point: \(9x_1 - 8x_2 \geq 0\)
- Maximum healthiness rating constraint: \(11x_1 + 12x_2 \leq 24\)

So, our symbolic representation is:
```json
{
  'sym_variables': [('x1', 'granola bars'), ('x2', 'steaks')],
  'objective_function': 'Minimize: x1 + 9*x2',
  'constraints': [
    '11*x1 + 12*x2 >= 9',
    '9*x1 - 8*x2 >= 0',
    '11*x1 + 12*x2 <= 24'
  ]
}
```

Now, let's implement this in Gurobi. We'll use Python for this purpose:

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Define variables - since the number of granola bars and steaks can be non-integer,
# we use continuous variable type
x1 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="granola_bars")
x2 = m.addVar(lb=0, vtype=GRB.CONTINUOUS, name="steaks")

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

# Add constraints
m.addConstr(11*x1 + 12*x2 >= 9, "min_healthiness")
m.addConstr(9*x1 - 8*x2 >= 0, "health_steak_ratio")
m.addConstr(11*x1 + 12*x2 <= 24, "max_healthiness")

# Optimize model
m.optimize()

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