## Step 1: Define the symbolic representation of the problem
Let's denote the number of packages of hammers as $x_1$ and the number of packages of screwdrivers as $x_2$. The objective is to maximize the profit $P = 72x_1 + 70x_2$. The constraints are based on the availability of the machines: $28x_1 + 23x_2 \leq 720$ for the lathe and $82x_1 + 76x_2 \leq 720$ for the CNG machine. Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of packages cannot be negative.

## Step 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for hammers
- $x_2$ for screwdrivers

The objective function is: $72x_1 + 70x_2$

The constraints are:
- $28x_1 + 23x_2 \leq 720$
- $82x_1 + 76x_2 \leq 720$
- $x_1 \geq 0$
- $x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'hammers'), ('x2', 'screwdrivers')],
'objective_function': '72*x1 + 70*x2',
'constraints': [
    '28*x1 + 23*x2 <= 720',
    '82*x1 + 76*x2 <= 720',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 3: Convert the problem into Gurobi code
To solve this problem using Gurobi, we will use the Gurobi Python API. First, ensure you have Gurobi installed in your Python environment.

```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(lb=0, name="hammers")
    x2 = model.addVar(lb=0, name="screwdrivers")

    # Define the objective function
    model.setObjective(72 * x1 + 70 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(28 * x1 + 23 * x2 <= 720, name="lathe_constraint")
    model.addConstr(82 * x1 + 76 * x2 <= 720, name="CNG_constraint")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of hammers: {x1.varValue}")
        print(f"Number of screwdrivers: {x2.varValue}")
        print(f"Maximum profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```