To solve the optimization problem described, we first need to define the symbolic representation of the variables and the objective function. Then, we translate the given constraints into algebraic expressions using these symbolic variables.

### Symbolic Representation:

Let's denote:
- \(x_0\) as the quantity of staplers,
- \(x_1\) as the quantity of color printers,
- \(x_2\) as the quantity of yellow highlighters.

The objective function is to maximize: \(1 \cdot x_0 + 9 \cdot x_1 + 9 \cdot x_2\).

### Constraints:

Based on the problem description, we have the following constraints:
1. Workplace safety impact from staplers and color printers: \(3x_0 + 9x_1 \leq 145\)
2. Workplace safety impact from staplers and yellow highlighters: \(3x_0 + 27x_2 \leq 176\)
3. Total workplace safety impact: \(3x_0 + 9x_1 + 27x_2 \leq 145\)
4. Weight of staplers, color printers, and yellow highlighters: \(9x_0 + 26x_1 + 32x_2 \geq 37\) (Note: This is a lower bound, not an upper bound as most constraints.)
5. Combined weight of color printers and yellow highlighters: \(26x_1 + 32x_2 \leq 185\)
6. Weight of staplers and yellow highlighters: \(9x_0 + 32x_2 \leq 173\)
7. Total weight of all items (redundant but listed for completeness): \(9x_0 + 26x_1 + 32x_2 \leq 173\)

### Integer Constraints:
- \(x_0, x_1, x_2\) are integers.

Given these constraints and the objective function, we can represent our problem symbolically as follows:

```json
{
    'sym_variables': [('x0', 'staplers'), ('x1', 'color printers'), ('x2', 'yellow highlighters')],
    'objective_function': '1*x0 + 9*x1 + 9*x2',
    'constraints': [
        '3*x0 + 9*x1 <= 145',
        '3*x0 + 27*x2 <= 176',
        '3*x0 + 9*x1 + 27*x2 <= 145',
        '9*x0 + 26*x1 + 32*x2 >= 37',
        '26*x1 + 32*x2 <= 185',
        '9*x0 + 32*x2 <= 173',
        '9*x0 + 26*x1 + 32*x2 <= 173'
    ]
}
```

Now, let's implement this optimization problem in Gurobi:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(vtype=GRB.INTEGER, name="staplers")
x1 = m.addVar(vtype=GRB.INTEGER, name="color_printers")
x2 = m.addVar(vtype=GRB.INTEGER, name="yellow_highlighters")

# Set the objective function
m.setObjective(1*x0 + 9*x1 + 9*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(3*x0 + 9*x1 <= 145)
m.addConstr(3*x0 + 27*x2 <= 176)
m.addConstr(3*x0 + 9*x1 + 27*x2 <= 145)
m.addConstr(9*x0 + 26*x1 + 32*x2 >= 37)
m.addConstr(26*x1 + 32*x2 <= 185)
m.addConstr(9*x0 + 32*x2 <= 173)
m.addConstr(9*x0 + 26*x1 + 32*x2 <= 173)

# Optimize the model
m.optimize()

# Print the solution
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print("Objective:", m.objVal)
```