To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints algebraically.

Let's define:
- $x_1$ as the number of 61-key synthesizer keyboards produced.
- $x_2$ as the number of 81-key synthesizer keyboards produced.

The objective is to maximize the total revenue, which can be calculated by multiplying the number of each type of keyboard produced by its selling price and then summing these products. Thus, the objective function is:
\[ \text{Maximize} \quad 1500x_1 + 2500x_2 \]

The constraints are based on the availability of oscillator chips and production time. For the oscillator chips, since each 61-key keyboard requires 8 chips and each 81-key keyboard requires 16 chips, and there are 3000 chips available, we have:
\[ 8x_1 + 16x_2 \leq 3000 \]

For the production time, given that both types of keyboards require 1.5 hours to produce and there are 8 working hours a day, we have:
\[ 1.5x_1 + 1.5x_2 \leq 8 \]

Additionally, since the number of keyboards produced cannot be negative, we have non-negativity constraints:
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

In symbolic notation with natural language descriptions, our problem formulation is:

```json
{
    'sym_variables': [('x1', 'Number of 61-key synthesizer keyboards'), 
                      ('x2', 'Number of 81-key synthesizer keyboards')],
    'objective_function': '1500*x1 + 2500*x2',
    'constraints': ['8*x1 + 16*x2 <= 3000', 
                    '1.5*x1 + 1.5*x2 <= 8', 
                    'x1 >= 0', 
                    'x2 >= 0']
}
```

To solve this linear programming problem, we can use Gurobi, a powerful optimization software, with Python as our interface. Here's how you could implement and solve the model in Python:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="x1", lb=0)  # Number of 61-key synthesizer keyboards
x2 = m.addVar(name="x2", lb=0)  # Number of 81-key synthesizer keyboards

# Set the objective function
m.setObjective(1500*x1 + 2500*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(8*x1 + 16*x2 <= 3000, name="OscillatorChipConstraint")
m.addConstr(1.5*x1 + 1.5*x2 <= 8, name="ProductionTimeConstraint")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of 61-key keyboards to produce: {x1.x}")
    print(f"Number of 81-key keyboards to produce: {x2.x}")
    print(f"Maximum revenue achievable: ${m.objVal}")
else:
    print("No optimal solution found. The model may be infeasible or unbounded.")
```