## Step 1: Define the symbolic representation of the problem
Let's denote the number of chocolate bars as $x_1$ and the number of chocolate wafers as $x_2$. The objective is to maximize the profit, which is $2x_1 + 3x_2$. The constraints are:
- $x_1 \leq 80$ (chocolate bar team production limit)
- $x_2 \leq 100$ (chocolate wafer team production limit)
- $x_1 + x_2 \leq 125$ (shared packaging machine limit)
- $x_1, x_2 \geq 0$ (non-negativity constraint, as the company cannot produce a negative number of items)

## Step 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for chocolate bars
- $x_2$ for chocolate wafers

The objective function is: $2x_1 + 3x_2$

The constraints are:
- $x_1 \leq 80$
- $x_2 \leq 100$
- $x_1 + x_2 \leq 125$
- $x_1, x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'chocolate bars'), ('x2', 'chocolate wafers')],
'objective_function': '2*x1 + 3*x2',
'constraints': ['x1 <= 80', 'x2 <= 100', 'x1 + x2 <= 125', 'x1 >= 0', 'x2 >= 0']
}
```

## Step 3: Translate the problem into Gurobi code
Now, let's translate this into Gurobi code in Python:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(lb=0, ub=80, name="chocolate_bars")
    x2 = model.addVar(lb=0, ub=100, name="chocolate_wafers")

    # Define the objective function
    model.setObjective(2*x1 + 3*x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(x1 <= 80, name="chocolate_bar_limit")
    model.addConstr(x2 <= 100, name="chocolate_wafer_limit")
    model.addConstr(x1 + x2 <= 125, name="packaging_machine_limit")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Chocolate bars: {x1.varValue}")
        print(f"Chocolate wafers: {x2.varValue}")
        print(f"Max profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_chocolate_production()
```