## Step 1: Define the symbolic representation of the problem
Let's denote the number of blankets as $x_1$ and the number of bedsheets as $x_2$. The objective is to maximize profit, which is $24x_1 + 21x_2$. The constraints are based on the time available for cutting and hemming. For cutting, the constraint is $14x_1 + 17x_2 \leq 2000$, and for hemming, it is $12x_1 + 14x_2 \leq 1500$. Also, $x_1 \geq 0$ and $x_2 \geq 0$ because the number of blankets and bedsheets cannot be negative.

## 2: Express the problem in the required symbolic format
The symbolic variables are:
- $x_1$ for blankets
- $x_2$ for bedsheets

The objective function is: $24x_1 + 21x_2$

The constraints are:
- $14x_1 + 17x_2 \leq 2000$
- $12x_1 + 14x_2 \leq 1500$
- $x_1 \geq 0$
- $x_2 \geq 0$

In the required format:
```json
{
'sym_variables': [('x1', 'blankets'), ('x2', 'bedsheets')],
'objective_function': '24*x1 + 21*x2',
'constraints': [
    '14*x1 + 17*x2 <= 2000',
    '12*x1 + 14*x2 <= 1500',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 3: Convert the problem into Gurobi code
To solve this problem using Gurobi in Python, we will use the following code:

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="blankets", lb=0, vtype=gurobi.GRB.CONTINUOUS)
    x2 = model.addVar(name="bedsheets", lb=0, vtype=gurobi.GRB.CONTINUOUS)

    # Set the objective function
    model.setObjective(24 * x1 + 21 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(14 * x1 + 17 * x2 <= 2000, name="cutting_constraint")
    model.addConstr(12 * x1 + 14 * x2 <= 1500, name="hemming_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of blankets: {x1.varValue}")
        print(f"Number of bedsheets: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_problem()
```