To solve this optimization problem, we first need to define the decision variables and the objective function. Let's denote:

- \(P\) as the number of premium consoles produced.
- \(R\) as the number of regular consoles produced.

The objective is to maximize profit. Given that each premium console generates $100 in profit and each regular console generates $75, our objective function can be written as:

\[ \text{Maximize:} \quad 100P + 75R \]

We have two main constraints based on the problem description:

1. **Testing Time Constraint**: The total testing time for both types of consoles cannot exceed 10,000 minutes. Given that a premium console takes 20 minutes to test and a regular console takes 10 minutes, we can express this constraint as:

\[ 20P + 10R \leq 10000 \]

2. **IC Chips Constraint**: The company has a limited supply of IC chips, with at most 1,500 chips available. Since a premium console requires 3 IC chips and a regular console requires 2 IC chips, we can express this constraint as:

\[ 3P + 2R \leq 1500 \]

Additionally, we have non-negativity constraints since the number of consoles produced cannot be negative:

\[ P \geq 0 \]
\[ R \geq 0 \]

Now, let's translate these into Gurobi code in Python. We will use the `gurobipy` library to formulate and solve this linear programming problem.

```python
from gurobipy import *

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

# Define decision variables
P = m.addVar(vtype=GRB.CONTINUOUS, name="PremiumConsoles", lb=0)
R = m.addVar(vtype=GRB.CONTINUOUS, name="RegularConsoles", lb=0)

# Objective function: Maximize profit
m.setObjective(100*P + 75*R, GRB.MAXIMIZE)

# Constraints
m.addConstr(20*P + 10*R <= 10000, "TestingTime")
m.addConstr(3*P + 2*R <= 1500, "ICChips")

# Solve the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Premium Consoles: {P.x}")
    print(f"Regular Consoles: {R.x}")
    print(f"Maximum Profit: ${100*P.x + 75*R.x:.2f}")
else:
    print("No optimal solution found")
```