To solve this optimization problem, we first need to define the decision variables, the objective function, and the constraints.

Let's denote:
- $x_{61}$ as the number of 61-key keyboards produced.
- $x_{81}$ as the number of 81-key keyboards produced.

The objective is to maximize the total revenue. Given that each 61-key keyboard is sold for $1500 and each 81-key keyboard is sold for $2500, the objective function can be written as:
\[ \text{Maximize} \quad 1500x_{61} + 2500x_{81} \]

There are two main constraints:
1. **Oscillator Chip Constraint**: The total number of oscillator chips used cannot exceed 3000. Since each 61-key keyboard requires 8 chips and each 81-key keyboard requires 16 chips, this constraint can be represented as:
\[ 8x_{61} + 16x_{81} \leq 3000 \]
2. **Production Time Constraint**: The total production time for both types of keyboards cannot exceed 8 hours. Given that each keyboard (regardless of type) requires 1.5 hours to produce, the constraint can be written as:
\[ 1.5x_{61} + 1.5x_{81} \leq 8 \]
Simplifying this, we get:
\[ x_{61} + x_{81} \leq \frac{8}{1.5} \]
\[ x_{61} + x_{81} \leq \frac{16}{3} \]

Additionally, the non-negativity constraints are:
\[ x_{61} \geq 0 \]
\[ x_{81} \geq 0 \]

Now, let's translate this into Gurobi code in Python:

```python
from gurobipy import *

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

# Define the decision variables
x61 = m.addVar(vtype=GRB.INTEGER, name="x61")
x81 = m.addVar(vtype=GRB.INTEGER, name="x81")

# Set the objective function
m.setObjective(1500*x61 + 2500*x81, GRB.MAXIMIZE)

# Add constraints
m.addConstr(8*x61 + 16*x81 <= 3000, "chip_constraint")
m.addConstr(x61 + x81 <= 16/3, "time_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("x61 =", x61.x)
    print("x81 =", x81.x)
    print("Objective function value:", m.objVal)
else:
    print("No optimal solution found")
```

```python
from gurobipy import *

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

# Define the decision variables
x61 = m.addVar(vtype=GRB.INTEGER, name="x61")
x81 = m.addVar(vtype=GRB.INTEGER, name="x81")

# Set the objective function
m.setObjective(1500*x61 + 2500*x81, GRB.MAXIMIZE)

# Add constraints
m.addConstr(8*x61 + 16*x81 <= 3000, "chip_constraint")
m.addConstr(x61 + x81 <= 16/3, "time_constraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("x61 =", x61.x)
    print("x81 =", x81.x)
    print("Objective function value:", m.objVal)
else:
    print("No optimal solution found")
```