## Step 1: Define the symbolic representation of the problem

Let's define the symbolic variables:
- $x_1$ represents the amount invested in the mining industry.
- $x_2$ represents the amount invested in the logging industry.

The objective is to maximize the return on investment. The return from the mining industry is 9% of $x_1$, and the return from the logging industry is 5% of $x_2$. Therefore, the objective function to maximize is $0.09x_1 + 0.05x_2$.

## Step 2: List the constraints

The constraints based on the problem description are:
1. The total investment is $100,000: $x_1 + x_2 = 100,000$.
2. The minimum investment in the mining industry is 30% of $100,000: $x_1 \geq 0.30 \times 100,000$.
3. The maximum investment in the logging industry is 55% of $100,000: $x_2 \leq 0.55 \times 100,000$.
4. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$.

However, since $x_1 + x_2 = 100,000$, the non-negativity constraints are implicitly considered within the bounds of the other constraints.

## 3: Express the problem in a symbolic notation

The symbolic representation is:
```json
{
'sym_variables': [('x1', 'mining industry investment'), ('x2', 'logging industry investment')],
'objective_function': '0.09*x1 + 0.05*x2',
'constraints': [
    'x1 + x2 = 100000',
    'x1 >= 30000',
    'x2 <= 55000'
]
}
```

## 4: Convert the problem into Gurobi code

Now, let's convert this into Gurobi code in Python:

```python
import gurobi

def solve_investment_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="mining_investment", lb=0)
    x2 = model.addVar(name="logging_investment", lb=0)

    # Objective function: maximize 0.09*x1 + 0.05*x2
    model.setObjective(0.09 * x1 + 0.05 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 == 100000, name="total_investment")
    model.addConstr(x1 >= 30000, name="min_mining_investment")
    model.addConstr(x2 <= 55000, name="max_logging_investment")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in mining industry: $", x1.varValue)
        print("Optimal investment in logging industry: $", x2.varValue)
        print("Maximum return: ", 0.09 * x1.varValue + 0.05 * x2.varValue)
    else:
        print("The model is infeasible")

solve_investment_problem()
```