To solve the given optimization problem using Gurobi, we first need to translate the natural language description into a mathematical formulation. The objective is to maximize the function $4.43x_0 + 3.06x_1$, where $x_0$ represents the number of monochrome printers and $x_1$ represents the quantity of postage stamps.

The constraints given are:
1. Usefulness rating of monochrome printers: $12x_0$
2. Usefulness rating of postage stamps: $13x_1$
3. Total usefulness rating must be greater than or equal to 38: $12x_0 + 13x_1 \geq 38$
4. Constraint on the combination of monochrome printers and postage stamps: $-8x_0 + 4x_1 \geq 0$
5. Total usefulness rating should be less than or equal to 72: $12x_0 + 13x_1 \leq 72$
6. Both $x_0$ and $x_1$ must be integers.

Given these constraints, we can now write the Gurobi code that captures this problem and provides a solution.

```python
from gurobipy import *

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

# Define variables
monochrome_printers = m.addVar(vtype=GRB.INTEGER, name="monochrome_printers")
postage_stamps = m.addVar(vtype=GRB.INTEGER, name="postage_stamps")

# Set the objective function
m.setObjective(4.43 * monochrome_printers + 3.06 * postage_stamps, GRB.MAXIMIZE)

# Add constraints
m.addConstr(12 * monochrome_printers + 13 * postage_stamps >= 38, name="usefulness_rating_min")
m.addConstr(-8 * monochrome_printers + 4 * postage_stamps >= 0, name="combination_constraint")
m.addConstr(12 * monochrome_printers + 13 * postage_stamps <= 72, name="usefulness_rating_max")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Monochrome printers: {monochrome_printers.x}")
    print(f"Postage stamps: {postage_stamps.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```