## Step 1: Define the symbolic representation of the variables
The variables are given as ['lightbulbs', 'paper towel rolls', 'cookies'], which can be symbolically represented as ['x1', 'x2', 'x3'].

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables and their corresponding natural language objects are:
- x1: lightbulbs
- x2: paper towel rolls
- x3: cookies

## Step 3: Define the objective function in symbolic notation
The objective function to minimize is: $8x_1 + 9x_2 + 6x_3$.

## Step 4: List the constraints in symbolic notation
The constraints given are:
- $16x_1 + 17x_2 \geq 34$
- $16x_1 + 8x_3 \geq 16$
- $16x_1 + 17x_2 + 8x_3 \geq 16$
- $-6x_1 + 6x_2 \geq 0$
- $5x_1 - 8x_3 \geq 0$
- $2x_2 - 9x_3 \geq 0$
- $x_1, x_2, x_3$ are whole numbers.

## 5: Create the symbolic representation of the problem
```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',
        'x1, x2, x3 are whole numbers'
    ]
}
```

## 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 the variables
    x1 = model.addVar(name='lightbulbs', vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name='paper towel rolls', vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name='cookies', vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(8 * x1 + 9 * x2 + 6 * x3, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(16 * x1 + 17 * x2 >= 34)
    model.addConstr(16 * x1 + 8 * x3 >= 16)
    model.addConstr(16 * x1 + 17 * x2 + 8 * x3 >= 16)
    model.addConstr(-6 * x1 + 6 * x2 >= 0)
    model.addConstr(5 * x1 - 8 * x3 >= 0)
    model.addConstr(2 * x2 - 9 * x3 >= 0)

    # Set dollar cost constraint 
    model.addConstr(16 * x1 + 17 * x2 <= 103)

    # Solve the model
    model.optimize()

    # Check if the model is optimized
    if model.status == gurobi.GRB.OPTIMAL:
        print('Optimal solution found.')
        print(f'lightbulbs: {x1.varValue}')
        print(f'paper towel rolls: {x2.varValue}')
        print(f'cookies: {x3.varValue}')
        print(f'Objective: {model.objVal}')
    else:
        print('No optimal solution found.')

solve_optimization_problem()
```