Here's the formulation of the Linear Program (LP) and the corresponding Gurobi code:

**Decision Variables:**

* `x_energy`: Amount invested in the energy industry.
* `x_telecom`: Amount invested in the telecom industry.
* `x_utilities`: Amount invested in the utilities industry.
* `x_healthcare`: Amount invested in the healthcare industry.

**Objective Function:**

Maximize the total return on investment:

```
Maximize: 0.05 * x_energy + 0.08 * x_telecom + 0.03 * x_utilities + 0.09 * x_healthcare
```

**Constraints:**

1. **Total Investment:** The total investment should equal the available funds ($300,000).

   ```
   x_energy + x_telecom + x_utilities + x_healthcare = 300000
   ```

2. **Healthcare Investment Limit:** The investment in healthcare cannot exceed the investment in energy.

   ```
   x_healthcare <= x_energy
   ```

3. **Telecom Investment Limit:** The investment in telecom cannot exceed the investment in utilities.

   ```
   x_telecom <= x_utilities
   ```

4. **Healthcare Percentage Limit:**  At most 33% of the total investment can be in healthcare.

   ```
   x_healthcare <= 0.33 * 300000 
   ```

5. **Non-negativity:** All investment amounts must be non-negative.

   ```
   x_energy, x_telecom, x_utilities, x_healthcare >= 0
   ```

```python
import gurobipy as gp
from gurobipy import GRB

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

# Create decision variables
x_energy = m.addVar(lb=0, name="x_energy")
x_telecom = m.addVar(lb=0, name="x_telecom")
x_utilities = m.addVar(lb=0, name="x_utilities")
x_healthcare = m.addVar(lb=0, name="x_healthcare")

# Set objective function
m.setObjective(0.05 * x_energy + 0.08 * x_telecom + 0.03 * x_utilities + 0.09 * x_healthcare, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x_energy + x_telecom + x_utilities + x_healthcare == 300000, "Total_Investment")
m.addConstr(x_healthcare <= x_energy, "Healthcare_Limit")
m.addConstr(x_telecom <= x_utilities, "Telecom_Limit")
m.addConstr(x_healthcare <= 0.33 * 300000, "Healthcare_Percentage_Limit")


# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal return: ${m.objVal:.2f}")
    print(f"Invest in Energy: ${x_energy.x:.2f}")
    print(f"Invest in Telecom: ${x_telecom.x:.2f}")
    print(f"Invest in Utilities: ${x_utilities.x:.2f}")
    print(f"Invest in Healthcare: ${x_healthcare.x:.2f}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
