## Step 1: Define the symbolic representation of the variables
The variables are: 
- 'red highlighters' 
- 'staplers' 
- 'manila envelopes' 
- 'monochrome printers'

Let's denote them as:
- $x_0$ for 'red highlighters'
- $x_1$ for 'staplers'
- $x_2$ for 'manila envelopes'
- $x_3$ for 'monochrome printers'

## Step 2: Formulate the objective function
The objective function to maximize is: 
\[ 4.5x_0 + 5.52x_1 + 1.67x_2 + 4.26x_3 \]

## 3: List the constraints
1. $10x_0 + 5x_3 \geq 25$
2. $5x_2 + 5x_3 \geq 21$
3. $13x_1 + 5x_2 \geq 18$
4. $10x_0 + 13x_1 \geq 28$
5. $10x_0 + 5x_2 \geq 18$
6. $10x_0 + 13x_1 + 5x_3 \geq 14$
7. $10x_0 + 5x_2 + 5x_3 \geq 14$
8. $10x_0 + 13x_1 + 5x_3 \geq 16$
9. $10x_0 + 5x_2 + 5x_3 \geq 16$
10. $x_0 + 12x_3 \leq 71$
11. $5x_1 + 12x_3 \leq 136$
12. $6x_2 + 12x_3 \leq 181$
13. $5x_1 + 6x_2 + 12x_3 \leq 48$
14. $x_0 + 6x_2 + 12x_3 \leq 187$
15. $x_0 + 5x_1 + 6x_2 + 12x_3 \leq 187$
16. $10x_0 + 5x_3 \leq 82$
17. $5x_2 + 5x_3 \leq 105$
18. $13x_1 + 5x_2 \leq 57$
19. $13x_1 + 5x_3 \leq 74$
20. $10x_0 + 13x_1 + 5x_3 \leq 36$
21. $10x_0 + 13x_1 + 5x_2 \leq 96$
22. $10x_0 + 13x_1 + 5x_2 + 5x_3 \leq 96$

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'red highlighters'),
        ('x1', 'staplers'),
        ('x2', 'manila envelopes'),
        ('x3', 'monochrome printers')
    ],
    'objective_function': '4.5*x0 + 5.52*x1 + 1.67*x2 + 4.26*x3',
    'constraints': [
        '10*x0 + 5*x3 >= 25',
        '5*x2 + 5*x3 >= 21',
        '13*x1 + 5*x2 >= 18',
        '10*x0 + 13*x1 >= 28',
        '10*x0 + 5*x2 >= 18',
        '10*x0 + 13*x1 + 5*x3 >= 14',
        '10*x0 + 5*x2 + 5*x3 >= 14',
        '10*x0 + 13*x1 + 5*x3 >= 16',
        '10*x0 + 5*x2 + 5*x3 >= 16',
        'x0 + 12*x3 <= 71',
        '5*x1 + 12*x3 <= 136',
        '6*x2 + 12*x3 <= 181',
        '5*x1 + 6*x2 + 12*x3 <= 48',
        'x0 + 6*x2 + 12*x3 <= 187',
        'x0 + 5*x1 + 6*x2 + 12*x3 <= 187',
        '10*x0 + 5*x3 <= 82',
        '5*x2 + 5*x3 <= 105',
        '13*x1 + 5*x2 <= 57',
        '13*x1 + 5*x3 <= 74',
        '10*x0 + 13*x1 + 5*x3 <= 36',
        '10*x0 + 13*x1 + 5*x2 <= 96',
        '10*x0 + 13*x1 + 5*x2 + 5*x3 <= 96'
    ]
}
```

## 5: Write the Gurobi code
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name="x0", vtype=gurobi.GRB.INTEGER)  # red highlighters
    x1 = model.addVar(name="x1", vtype=gurobi.GRB.INTEGER)  # staplers
    x2 = model.addVar(name="x2", vtype=gurobi.GRB.INTEGER)  # manila envelopes
    x3 = model.addVar(name="x3", vtype=gurobi.GRB.INTEGER)  # monochrome printers

    # Define the objective function
    model.setObjective(4.5 * x0 + 5.52 * x1 + 1.67 * x2 + 4.26 * x3, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(10 * x0 + 5 * x3 >= 25)
    model.addConstr(5 * x2 + 5 * x3 >= 21)
    model.addConstr(13 * x1 + 5 * x2 >= 18)
    model.addConstr(10 * x0 + 13 * x1 >= 28)
    model.addConstr(10 * x0 + 5 * x2 >= 18)
    model.addConstr(10 * x0 + 13 * x1 + 5 * x3 >= 14)
    model.addConstr(10 * x0 + 5 * x2 + 5 * x3 >= 14)
    model.addConstr(10 * x0 + 13 * x1 + 5 * x3 >= 16)
    model.addConstr(10 * x0 + 5 * x2 + 5 * x3 >= 16)
    model.addConstr(x0 + 12 * x3 <= 71)
    model.addConstr(5 * x1 + 12 * x3 <= 136)
    model.addConstr(6 * x2 + 12 * x3 <= 181)
    model.addConstr(5 * x1 + 6 * x2 + 12 * x3 <= 48)
    model.addConstr(x0 + 6 * x2 + 12 * x3 <= 187)
    model.addConstr(x0 + 5 * x1 + 6 * x2 + 12 * x3 <= 187)
    model.addConstr(10 * x0 + 5 * x3 <= 82)
    model.addConstr(5 * x2 + 5 * x3 <= 105)
    model.addConstr(13 * x1 + 5 * x2 <= 57)
    model.addConstr(13 * x1 + 5 * x3 <= 74)
    model.addConstr(10 * x0 + 13 * x1 + 5 * x3 <= 36)
    model.addConstr(10 * x0 + 13 * x1 + 5 * x2 <= 96)
    model.addConstr(10 * x0 + 13 * x1 + 5 * x2 + 5 * x3 <= 96)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objval)
        print("x0: ", x0.varValue)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

solve_optimization_problem()
```