To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. The variables are 'lightbulbs', 'paper towel rolls', and 'cookies'. Let's denote these as $x_1$, $x_2$, and $x_3$ respectively.

The objective function is to minimize $8x_1 + 9x_2 + 6x_3$.

The constraints are:
- $16x_1 + 17x_2 \geq 34$
- $16x_1 + 8x_3 \geq 16$
- $16x_1 + 17x_2 + 8x_3 \geq 16$
- $-6x_1 + 6x_2 \geq 0$
- $5x_1 - 8x_3 \geq 0$
- $2x_2 - 9x_3 \geq 0$
- $x_1$, $x_2$, and $x_3$ must be whole numbers.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'lightbulbs'), ('x2', 'paper towel rolls'), ('x3', 'cookies')],
    'objective_function': '8*x1 + 9*x2 + 6*x3',
    'constraints': [
        '16*x1 + 17*x2 >= 34',
        '16*x1 + 8*x3 >= 16',
        '16*x1 + 17*x2 + 8*x3 >= 16',
        '-6*x1 + 6*x2 >= 0',
        '5*x1 - 8*x3 >= 0',
        '2*x2 - 9*x3 >= 0'
    ]
}
```

Now, let's write the Gurobi code to solve this problem:
```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(vtype=GRB.INTEGER, name="lightbulbs")
x2 = m.addVar(vtype=GRB.INTEGER, name="paper_towel_rolls")
x3 = m.addVar(vtype=GRB.INTEGER, name="cookies")

# Set the objective function
m.setObjective(8*x1 + 9*x2 + 6*x3, GRB.MINIMIZE)

# Add constraints
m.addConstr(16*x1 + 17*x2 >= 34, "constraint_1")
m.addConstr(16*x1 + 8*x3 >= 16, "constraint_2")
m.addConstr(16*x1 + 17*x2 + 8*x3 >= 16, "constraint_3")
m.addConstr(-6*x1 + 6*x2 >= 0, "constraint_4")
m.addConstr(5*x1 - 8*x3 >= 0, "constraint_5")
m.addConstr(2*x2 - 9*x3 >= 0, "constraint_6")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("lightbulbs:", x1.x)
    print("paper_towel_rolls:", x2.x)
    print("cookies:", x3.x)
else:
    print("No optimal solution found.")
```