## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the number of taro bubble teas
- $x_2$ represents the number of mango bubble teas

The objective function to maximize profit is: $4x_1 + 6x_2$

The constraints based on the available ingredients are:
- $3x_1 \leq 60$ (taro constraint)
- $3x_2 \leq 60$ (mango constraint)
- $4x_1 + 5x_2 \leq 140$ (milk constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## Step 2: Convert the symbolic representation into a Gurobi code

```json
{
'sym_variables': [('x1', 'taro bubble teas'), ('x2', 'mango bubble teas')],
'objective_function': '4*x1 + 6*x2',
'constraints': [
    '3*x1 <= 60',
    '3*x2 <= 60',
    '4*x1 + 5*x2 <= 140',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 3: Write the Gurobi code in Python

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="taro_bubble_tea", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="mango_bubble_tea", lb=0, ub=gurobi.GRB.INFINITY)

    # Define the objective function
    model.setObjective(4 * x1 + 6 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(3 * x1 <= 60, name="taro_constraint")
    model.addConstr(3 * x2 <= 60, name="mango_constraint")
    model.addConstr(4 * x1 + 5 * x2 <= 140, name="milk_constraint")

    # Optimize the model
    model.optimize()

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

solve_bubble_tea_problem()
```