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

**Decision Variables:**

* `x`: Number of drill packages manufactured.
* `y`: Number of saw packages manufactured.

**Objective Function:**

Maximize profit: `35x + 100y`

**Constraints:**

* Milling machine time: `20x + 30y <= 800`
* CNG machine time: `70x + 90y <= 800`
* Non-negativity: `x >= 0`, `y >= 0`


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

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

# Create variables
x = m.addVar(vtype=GRB.CONTINUOUS, name="drills")  # Number of drill packages
y = m.addVar(vtype=GRB.CONTINUOUS, name="saws")  # Number of saw packages

# Set objective function
m.setObjective(35*x + 100*y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(20*x + 30*y <= 800, "milling_constraint")
m.addConstr(70*x + 90*y <= 800, "cng_constraint")
m.addConstr(x >= 0, "x_nonneg")
m.addConstr(y >= 0, "y_nonneg")

# Optimize model
m.optimize()

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

```
