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

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 function aims to maximize profit. Given that each CPU brings a profit of $300 and each GPU brings a profit of $500, the objective function can be written as:
\[ \text{Maximize:} \quad 300x_1 + 500x_2 \]

The constraints are based on the available time for wiring and soldering. For wiring, each CPU requires 30 minutes, and each GPU requires 90 minutes, with a total of 2000 minutes available:
\[ 30x_1 + 90x_2 \leq 2000 \]

For soldering, each CPU requires 50 minutes, and each GPU requires 40 minutes, with a total of 2500 minutes available:
\[ 50x_1 + 40x_2 \leq 2500 \]

Additionally, the number of CPUs and GPUs cannot be negative (non-negativity constraint):
\[ x_1 \geq 0, \quad x_2 \geq 0 \]

Thus, the symbolic representation of the problem can be summarized as:
```json
{
    'sym_variables': [('x1', 'Number of CPUs'), ('x2', 'Number of GPUs')],
    'objective_function': '300*x1 + 500*x2',
    'constraints': ['30*x1 + 90*x2 <= 2000', '50*x1 + 40*x2 <= 2500', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we'll need to install the Gurobi library if it's not already installed. The code below sets up and solves the optimization problem:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(300*x1 + 500*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(30*x1 + 90*x2 <= 2000, "Wiring_Time")
m.addConstr(50*x1 + 40*x2 <= 2500, "Soldering_Time")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print(f"Optimal solution: Produce {x1.x} CPUs and {x2.x} GPUs")
else:
    print("No optimal solution found")
```