## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'bananas' and 'granola bars', which we can represent symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $1 \times \text{bananas} + 8 \times \text{granola bars}$, which in symbolic notation is $1x_1 + 8x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- $14x_1 + 8x_2 \geq 38$ (spend at least $38 on bananas and granola bars)
- $11x_1 + 9x_2 \geq 145$ (total healthiness rating must be greater than or equal to 145)
- $-4x_1 + 9x_2 \geq 0$ (a specific relationship between bananas and granola bars)
- $14x_1 + 8x_2 \leq 111$ (maximum spend of $111 on bananas and granola bars, repeated)
- $11x_1 + 9x_2 \leq 255$ (total combined healthiness rating must be 255 or less)

## 4: Consider variable bounds and types
- $x_1$ (bananas) can be a non-whole number.
- $x_2$ (granola bars) must be a whole number.

## 5: Express the problem in a suitable format for Gurobi
The symbolic representation of the problem is:
```json
{
'sym_variables': [('x1', 'bananas'), ('x2', 'granola bars')],
'objective_function': '1*x1 + 8*x2',
'constraints': [
    '14*x1 + 8*x2 >= 38',
    '11*x1 + 9*x2 >= 145',
    '-4*x1 + 9*x2 >= 0',
    '14*x1 + 8*x2 <= 111',
    '11*x1 + 9*x2 <= 255'
]
}
```

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

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

    # Define variables
    x1 = model.addVar(name="bananas", lb=0)  # No lower bound specified, assuming 0
    x2 = model.addVar(name="granola_bars", lb=0, integrality=gurobi.GRB.INTEGER)  # Granola bars must be an integer

    # Objective function: maximize 1*bananas + 8*granola_bars
    model.setObjective(x1 + 8*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(14*x1 + 8*x2 >= 38, name="min_spend")
    model.addConstr(11*x1 + 9*x2 >= 145, name="min_healthiness")
    model.addConstr(-4*x1 + 9*x2 >= 0, name="relationship")
    model.addConstr(14*x1 + 8*x2 <= 111, name="max_spend")
    model.addConstr(11*x1 + 9*x2 <= 255, name="max_healthiness")

    # Optimize the model
    model.optimize()

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

solve_optimization_problem()
```