To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. Let's denote the number of blankets as $x_1$ and the number of bedsheets as $x_2$. The objective is to maximize profit.

The profit per blanket is $24, and the profit per bedsheet is $21. Therefore, the objective function can be written as:
\[ 24x_1 + 21x_2 \]

Now, let's consider the constraints:

1. Each blanket takes 14 minutes to cut, and each bedsheet takes 17 minutes to cut. There are 2000 minutes available for cutting.
\[ 14x_1 + 17x_2 \leq 2000 \]

2. Each blanket takes 12 minutes to hem, and each bedsheet takes 14 minutes to hem. There are 1500 minutes available for hemming.
\[ 12x_1 + 14x_2 \leq 1500 \]

3. Non-negativity constraints: The number of blankets and bedsheets cannot be negative.
\[ x_1 \geq 0, x_2 \geq 0 \]

The symbolic representation of the problem can be summarized as:
```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']
}
```

Now, let's write the Gurobi code to solve this optimization problem:
```python
from gurobipy import *

# Create a model
m = Model("Blankets_and_Bedsheets")

# Add variables
x1 = m.addVar(lb=0, vtype=GRB.INTEGER, name="blankets")
x2 = m.addVar(lb=0, vtype=GRB.INTEGER, name="bedsheets")

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

# Add constraints
m.addConstr(14*x1 + 17*x2 <= 2000, "cutting_time")
m.addConstr(12*x1 + 14*x2 <= 1500, "hemming_time")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Blankets: {x1.x}")
    print(f"Bedsheets: {x2.x}")
    print(f"Maximum profit: {m.objVal}")
else:
    print("No optimal solution found")
```