```json
{
  "sym_variables": [
    ("x1", "tennis rackets"),
    ("x2", "badminton rackets")
  ],
  "objective_function": "20*x1 + 17*x2",
  "constraints": [
    "12*x1 + 10*x2 <= 3000",  // Molding time constraint
    "15*x1 + 12*x2 <= 3500",  // Stringing time constraint
    "x1 >= 0",                // Non-negativity constraint
    "x2 >= 0"                 // Non-negativity constraint
  ]
}
```

```python
import gurobipy as gp

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

# Create variables
tennis_rackets = m.addVar(vtype=gp.GRB.CONTINUOUS, name="tennis_rackets")  # x1
badminton_rackets = m.addVar(vtype=gp.GRB.CONTINUOUS, name="badminton_rackets")  # x2


# Set objective function
m.setObjective(20 * tennis_rackets + 17 * badminton_rackets, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(12 * tennis_rackets + 10 * badminton_rackets <= 3000, "molding_constraint")
m.addConstr(15 * tennis_rackets + 12 * badminton_rackets <= 3500, "stringing_constraint")
m.addConstr(tennis_rackets >= 0, "tennis_non_negativity")
m.addConstr(badminton_rackets >= 0, "badminton_non_negativity")

# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Number of Tennis Rackets: {tennis_rackets.x}")
    print(f"Number of Badminton Rackets: {badminton_rackets.x}")
    print(f"Maximum Profit: ${m.objVal}")
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
