## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- $x_1$ as the number of CPUs to be made
- $x_2$ as the number of GPUs to be made

The objective is to maximize profit, where the profit per CPU is $300 and per GPU is $500. Therefore, the objective function can be represented as:

Maximize $300x_1 + 500x_2$

The constraints based on the given resources are:

- Wiring time: $30x_1 + 90x_2 \leq 2000$
- Soldering time: $50x_1 + 40x_2 \leq 2500$
- Non-negativity: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'CPUs'), ('x2', 'GPUs')],
    'objective_function': '300*x1 + 500*x2',
    'constraints': [
        '30*x1 + 90*x2 <= 2000',
        '50*x1 + 40*x2 <= 2500',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="CPUs", lb=0, vtype=gp.GRB.INTEGER)  # Number of CPUs
x2 = model.addVar(name="GPUs", lb=0, vtype=gp.GRB.INTEGER)  # Number of GPUs

# Objective function: Maximize profit
model.setObjective(300*x1 + 500*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(30*x1 + 90*x2 <= 2000, name="Wiring_Time")  # Wiring time constraint
model.addConstr(50*x1 + 40*x2 <= 2500, name="Soldering_Time")  # Soldering time constraint

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: CPUs = {x1.varValue}, GPUs = {x2.varValue}")
    print(f"Max Profit: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```