To solve the given optimization problem, 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.

### Symbolic Representation:

Let's define:
- $x_1$ as the amount of money invested in the textile industry.
- $x_2$ as the amount of money invested in the telecom industry.

The objective is to maximize the return on investment, which can be represented by the objective function:
\[ \text{Maximize: } 0.06x_1 + 0.08x_2 \]

Given constraints are:
1. The total investment is $500,000.
\[ x_1 + x_2 = 500,000 \]
2. At least 30% of the investment should be in the textile industry.
\[ x_1 \geq 0.3 \times 500,000 \]
\[ x_1 \geq 150,000 \]
3. At most 50% of the investment should be in the telecom industry.
\[ x_2 \leq 0.5 \times 500,000 \]
\[ x_2 \leq 250,000 \]

### Symbolic Representation in JSON Format:

```json
{
    'sym_variables': [('x1', 'amount invested in textile industry'), ('x2', 'amount invested in telecom industry')],
    'objective_function': '0.06*x1 + 0.08*x2',
    'constraints': [
        'x1 + x2 == 500000',
        'x1 >= 150000',
        'x2 <= 250000',
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

### Gurobi Code:

To solve this linear programming problem using Gurobi in Python, we use the following code:

```python
from gurobipy import *

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

# Define variables
x1 = m.addVar(lb=0, name="textile_investment")
x2 = m.addVar(lb=0, name="telecom_investment")

# Set the objective function
m.setObjective(0.06*x1 + 0.08*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 500000, "total_investment")
m.addConstr(x1 >= 150000, "min_textile_investment")
m.addConstr(x2 <= 250000, "max_telecom_investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found.")
    print("Textile Investment: $", x1.x)
    print("Telecom Investment: $", x2.x)
else:
    print("No optimal solution found.")
```