## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ = Number of premium consoles
- $x_2$ = Number of regular consoles

The objective function is to maximize profit:
- Profit per premium console = $100
- Profit per regular console = $75

So, the objective function can be represented as:
Maximize $100x_1 + 75x_2$

The constraints are:
- Testing time: $20x_1 + 10x_2 \leq 10000$
- IC chips: $3x_1 + 2x_2 \leq 1500$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'premium consoles'), ('x2', 'regular consoles')],
    'objective_function': '100*x1 + 75*x2',
    'constraints': [
        '20*x1 + 10*x2 <= 10000',
        '3*x1 + 2*x2 <= 1500',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("console_production")

# Define variables
x1 = model.addVar(name="premium_consoles", lb=0, vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="regular_consoles", lb=0, vtype=gp.GRB.INTEGER)

# Objective function: Maximize profit
model.setObjective(100*x1 + 75*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(20*x1 + 10*x2 <= 10000, name="testing_time")
model.addConstr(3*x1 + 2*x2 <= 1500, name="ic_chips")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print(f"Number of premium consoles: {x1.varValue}")
    print(f"Number of regular consoles: {x2.varValue}")
    print(f"Max profit: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```