## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's define the variables and the objective function, and list the constraints.

### Variables
- $x_1$: Number of regular tables produced per day
- $x_2$: Number of standing tables produced per day

### Objective Function
The objective is to maximize profit. Each regular table generates a profit of $150, and each standing table generates a profit of $180. Therefore, the objective function can be represented as:
\[ \text{Maximize:} \quad 150x_1 + 180x_2 \]

### Constraints
1. Team A can produce at most 25 regular tables per day: $x_1 \leq 25$
2. Team B can produce at most 50 standing tables per day: $x_2 \leq 50$
3. The woodworking machine can make at most 60 total tables: $x_1 + x_2 \leq 60$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'regular tables'), ('x2', 'standing tables')],
    'objective_function': '150*x1 + 180*x2',
    'constraints': [
        'x1 <= 25',
        'x2 <= 50',
        'x1 + x2 <= 60',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define variables
x1 = model.addVar(name="regular_tables", lb=0, ub=25, vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="standing_tables", lb=0, ub=50, vtype=gp.GRB.INTEGER)

# Objective function: Maximize profit
model.setObjective(150*x1 + 180*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 <= 25, name="team_A_capacity")
model.addConstr(x2 <= 50, name="team_B_capacity")
model.addConstr(x1 + x2 <= 60, name="woodworking_machine_capacity")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found. Produce {x1.varValue} regular tables and {x2.varValue} standing tables.")
    print(f"Maximum profit: ${150*x1.varValue + 180*x2.varValue}")
else:
    print("No optimal solution found.")
```