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

Let's define:
- $x_1$ as the amount invested in the paper industry.
- $x_2$ as the amount invested in the glass industry.

The objective is to maximize the return on investments, which can be represented by the objective function: $0.02x_1 + 0.05x_2$, since a 2% return from the paper industry and a 5% return from the glass industry are expected.

The constraints based on the problem description are:
1. The total investment is $300,000: $x_1 + x_2 = 300,000$.
2. At least 30% of the investment must be in the paper industry: $x_1 \geq 0.3 \times 300,000$ or $x_1 \geq 90,000$.
3. At most 50% of the investment can be in the glass industry: $x_2 \leq 0.5 \times 300,000$ or $x_2 \leq 150,000$.
4. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$, since the amount invested cannot be negative.

The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount invested in paper industry'), ('x2', 'amount invested in glass industry')],
    'objective_function': 'maximize 0.02*x1 + 0.05*x2',
    'constraints': [
        'x1 + x2 = 300000',
        'x1 >= 90000',
        'x2 <= 150000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

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

```python
from gurobipy import *

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

# Define the variables
x1 = m.addVar(lb=0, name="Paper_Industry_Investment")
x2 = m.addVar(lb=0, name="Glass_Industry_Investment")

# Set the objective function
m.setObjective(0.02*x1 + 0.05*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 300000, "Total_Investment")
m.addConstr(x1 >= 90000, "Minimum_Paper_Industry_Investment")
m.addConstr(x2 <= 150000, "Maximum_Glass_Industry_Investment")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Amount invested in Paper Industry: {x1.x}")
    print(f"Amount invested in Glass Industry: {x2.x}")
else:
    print("No optimal solution found")
```