## Step 1: Define the symbolic representation of the variables
The variables are 'staplers', 'color printers', and 'yellow highlighters', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the objective function into symbolic notation
The objective function to maximize is $1 \cdot x_0 + 9 \cdot x_1 + 9 \cdot x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints given are:
- $3x_0 + 9x_1 + 27x_2 \leq 187$ (workplace safety impact)
- $9x_0 + 26x_1 + 32x_2 \leq 220$ (weight)
- $9x_0 + 26x_1 + 32x_2 \geq 37$ (combined weight of at least 37 lbs)
- $3x_0 + 27x_2 \leq 176$ (total combined workplace safety impact from staplers and yellow highlighters)
- $3x_0 + 9x_1 \leq 145$ (total combined workplace safety impact from staplers and color printers)
- $3x_0 + 9x_1 + 27x_2 \leq 145$ (total combined workplace safety impact from all)
- $26x_1 + 32x_2 \leq 185$ (color printers and yellow highlighters weight)
- $9x_0 + 32x_2 \leq 173$ (staplers and yellow highlighters weight)
- $9x_0 + 26x_1 + 32x_2 \leq 173$ (total weight of all)

## 4: Specify the variable bounds and types
- $x_0$ is an integer (staplers)
- $x_1$ is an integer (color printers)
- $x_2$ is an integer (yellow highlighters)

## 5: Create the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'staplers'), ('x1', 'color printers'), ('x2', 'yellow highlighters')],
    'objective_function': '1*x0 + 9*x1 + 9*x2',
    'constraints': [
        '3*x0 + 9*x1 + 27*x2 <= 187',
        '9*x0 + 26*x1 + 32*x2 <= 220',
        '9*x0 + 26*x1 + 32*x2 >= 37',
        '3*x0 + 27*x2 <= 176',
        '3*x0 + 9*x1 <= 145',
        '3*x0 + 9*x1 + 27*x2 <= 145',
        '26*x1 + 32*x2 <= 185',
        '9*x0 + 32*x2 <= 173',
        '9*x0 + 26*x1 + 32*x2 <= 173'
    ]
}
```

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

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    staplers = model.addVar(name="staplers", vtype=gurobi.GRB.INTEGER)
    color_printers = model.addVar(name="color_printers", vtype=gurobi.GRB.INTEGER)
    yellow_highlighters = model.addVar(name="yellow_highlighters", vtype=gurobi.GRB.INTEGER)

    # Define objective function
    model.setObjective(staplers + 9 * color_printers + 9 * yellow_highlighters, gurobi.GRB.MAXIMIZE)

    # Define constraints
    model.addConstr(3 * staplers + 9 * color_printers + 27 * yellow_highlighters <= 187, name="workplace_safety_impact")
    model.addConstr(9 * staplers + 26 * color_printers + 32 * yellow_highlighters <= 220, name="weight")
    model.addConstr(9 * staplers + 26 * color_printers + 32 * yellow_highlighters >= 37, name="combined_weight")
    model.addConstr(3 * staplers + 27 * yellow_highlighters <= 176, name="staplers_yellow_highlighters_safety")
    model.addConstr(3 * staplers + 9 * color_printers <= 145, name="staplers_color_printers_safety")
    model.addConstr(3 * staplers + 9 * color_printers + 27 * yellow_highlighters <= 145, name="all_safety")
    model.addConstr(26 * color_printers + 32 * yellow_highlighters <= 185, name="color_printers_yellow_highlighters_weight")
    model.addConstr(9 * staplers + 32 * yellow_highlighters <= 173, name="staplers_yellow_highlighters_weight")
    model.addConstr(9 * staplers + 26 * color_printers + 32 * yellow_highlighters <= 173, name="all_weight")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Staplers: {staplers.varValue}")
        print(f"Color Printers: {color_printers.varValue}")
        print(f"Yellow Highlighters: {yellow_highlighters.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```