To solve the optimization problem described, we first need to convert the natural language description into a symbolic representation. This involves defining variables and translating the objective function and constraints into algebraic terms.

Let's define the variables as follows:
- $x_1$: Amount invested in the energy industry
- $x_2$: Amount invested in the telecom industry
- $x_3$: Amount invested in the utilities industry
- $x_4$: Amount invested in the health care industry

The objective function is to maximize the return on investment, which can be represented as:
0.05$x_1$ + 0.08$x_2$ + 0.03$x_3$ + 0.09$x_4$

The constraints based on the problem description are:
1. $x_1 + x_2 + x_3 + x_4 \leq 300000$ (Total investment cannot exceed $300,000)
2. $x_4 \leq x_1$ (Amount invested in health care cannot exceed the amount invested in energy)
3. $x_2 \leq x_3$ (Amount invested in telecom cannot exceed the amount invested in utilities)
4. $x_4 \leq 0.33 \times 300000$ (At most 33% of the total amount can be invested in health care)

Symbolic representation:
```json
{
    'sym_variables': [('x1', 'energy'), ('x2', 'telecom'), ('x3', 'utilities'), ('x4', 'health care')],
    'objective_function': '0.05*x1 + 0.08*x2 + 0.03*x3 + 0.09*x4',
    'constraints': [
        'x1 + x2 + x3 + x4 <= 300000',
        'x4 <= x1',
        'x2 <= x3',
        'x4 <= 0.33 * 300000'
    ]
}
```

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

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(name="energy", lb=0)
x2 = m.addVar(name="telecom", lb=0)
x3 = m.addVar(name="utilities", lb=0)
x4 = m.addVar(name="health_care", lb=0)

# Set the objective function
m.setObjective(0.05*x1 + 0.08*x2 + 0.03*x3 + 0.09*x4, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 + x3 + x4 <= 300000, name="total_investment")
m.addConstr(x4 <= x1, name="health_care_vs_energy")
m.addConstr(x2 <= x3, name="telecom_vs_utilities")
m.addConstr(x4 <= 0.33 * 300000, name="max_health_care")

# Optimize the model
m.optimize()

# Print the results
for v in m.getVars():
    print(f"{v.varName}: {v.x}")

print(f"Objective Function Value: {m.objVal}")
```