## Problem Description and Formulation

The problem described is a classic example of a linear programming problem. ABC Toys Co wants to maximize its profit by producing the optimal number of RC drones and model cars given the constraints on wood and paint availability.

Let's define the decision variables:
- \(x\): Number of RC drones to produce
- \(y\): Number of model cars to produce

The objective function to maximize profit is:
\[ \text{Maximize:} \quad 50x + 90y \]

Subject to the constraints:
1. Wood constraint: \( 7x + 4y \leq 200 \)
2. Paint constraint: \( 30x + 20y \leq 900 \)
3. Non-negativity constraints: \( x \geq 0, y \geq 0 \)

## Gurobi Code

To solve this problem using Gurobi in Python, we will use the Gurobi Python API. First, ensure you have Gurobi installed in your environment. You can install it via pip:
```bash
pip install gurobi
```

Here's the Gurobi code for the problem:

```python
import gurobi as gp

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

# Define the decision variables
x = model.addVar(name="RC_Drones", lb=0, vtype=gp.GRB.INTEGER)  # Number of RC drones
y = model.addVar(name="Model_Cars", lb=0, vtype=gp.GRB.INTEGER)  # Number of model cars

# Define the objective function
model.setObjective(50*x + 90*y, sense=gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(7*x + 4*y <= 200, name="Wood_Constraint")
model.addConstr(30*x + 20*y <= 900, name="Paint_Constraint")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution found. Number of RC drones to produce: {x.varValue}, Number of model cars to produce: {y.varValue}")
    print(f"Max Profit: ${model.objVal:.2f}")
else:
    print("No optimal solution found.")
```

This code defines the optimization problem as described, solves it using Gurobi, and prints out the optimal production levels for RC drones and model cars, along with the maximum achievable profit. If no optimal solution is found (for example, if the problem is infeasible), it will indicate that as well.