To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables for the quantities of regular and portable speakers made by each team.

Let's define:
- $x_1$ as the number of regular speakers made by Team A.
- $x_2$ as the number of portable speakers made by Team B.

The objective function is to maximize profit, which is given by $40x_1 + 60x_2$ since the profit per regular speaker is $40 and per portable speaker is $60.

The constraints are:
1. Team A can make at most 120 regular speakers per day: $x_1 \leq 120$.
2. Team B can make at most 200 portable speakers per day: $x_2 \leq 200$.
3. The shared testing machine can be used to make a maximum of 300 total speakers per day: $x_1 + x_2 \leq 300$.

Non-negativity constraints also apply since the number of speakers cannot be negative:
- $x_1 \geq 0$
- $x_2 \geq 0$

Thus, the symbolic representation in JSON format is:

```json
{
    'sym_variables': [('x1', 'number of regular speakers made by Team A'), ('x2', 'number of portable speakers made by Team B')],
    'objective_function': '40*x1 + 60*x2',
    'constraints': ['x1 <= 120', 'x2 <= 200', 'x1 + x2 <= 300', 'x1 >= 0', 'x2 >= 0']
}
```

To solve this problem using Gurobi in Python, we'll use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="regular_speakers")
x2 = m.addVar(lb=0, name="portable_speakers")

# Set the objective function
m.setObjective(40*x1 + 60*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 <= 120, "Team_A_Capacity")
m.addConstr(x2 <= 200, "Team_B_Capacity")
m.addConstr(x1 + x2 <= 300, "Testing_Machine_Capacity")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Regular speakers: {x1.x}")
    print(f"Portable speakers: {x2.x}")
    print(f"Maximum profit: ${40*x1.x + 60*x2.x}")
else:
    print("No optimal solution found")
```