Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Number of CPUs manufactured.
* `y`: Number of GPUs manufactured.

**Objective Function:**

Maximize profit: `300x + 500y`

**Constraints:**

* Wiring constraint: `30x + 90y <= 2000`
* Soldering constraint: `50x + 40y <= 2500`
* Non-negativity constraints: `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

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

# Create decision variables
x = model.addVar(vtype=GRB.INTEGER, name="CPUs")  # Integer number of CPUs
y = model.addVar(vtype=GRB.INTEGER, name="GPUs")  # Integer number of GPUs

# Set the objective function
model.setObjective(300*x + 500*y, GRB.MAXIMIZE)

# Add constraints
model.addConstr(30*x + 90*y <= 2000, "Wiring_Constraint")
model.addConstr(50*x + 40*y <= 2500, "Soldering_Constraint")


# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of CPUs to manufacture: {x.x}")
    print(f"Number of GPUs to manufacture: {y.x}")
    print(f"Maximum Profit: ${model.objVal}")
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
