To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation that can be used to formulate a linear programming (LP) model. The objective of the LP is to maximize the return on investment.

Let's define the decision variables as follows:
- \(E\): Amount invested in the energy industry
- \(T\): Amount invested in the telecom industry
- \(U\): Amount invested in the utilities industry
- \(H\): Amount invested in the health care industry

The given constraints can be translated into the following symbolic representations:
1. The total amount invested cannot exceed $300,000: \(E + T + U + H \leq 300,000\)
2. The amount invested in the health care industry cannot exceed the amount invested in the energy industry: \(H \leq E\)
3. The amount invested in the telecom industry cannot exceed the amount invested in the utilities industry: \(T \leq U\)
4. At most 33% of the total amount of money can be invested in the health care industry: \(H \leq 0.33 \times (E + T + U + H)\)

The objective function to maximize the return on investment, given the annual rates of return for each industry (energy = 5%, telecom = 8%, utilities = 3%, health care = 9%), can be represented as:
\[Maximize: 0.05E + 0.08T + 0.03U + 0.09H\]

Now, let's implement this LP model using Gurobi in Python:

```python
from gurobipy import *

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

# Define the decision variables
E = m.addVar(lb=0, name="Energy")
T = m.addVar(lb=0, name="Telecom")
U = m.addVar(lb=0, name="Utilities")
H = m.addVar(lb=0, name="Health_Care")

# Define the constraints
m.addConstr(E + T + U + H <= 300000, "Total_Investment")
m.addConstr(H <= E, "Health_Care_VS_Energy")
m.addConstr(T <= U, "Telecom_VS_Utilities")
m.addConstr(H <= 0.33 * (E + T + U + H), "Max_Health_Care_Percentage")

# Define the objective function
m.setObjective(0.05*E + 0.08*T + 0.03*U + 0.09*H, GRB.MAXIMIZE)

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Energy: {E.x}")
    print(f"Telecom: {T.x}")
    print(f"Utilities: {U.x}")
    print(f"Health Care: {H.x}")
    print(f"Total Return: {m.objVal}")
else:
    print("No optimal solution found")
```