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

Let's denote the amount invested in the avocado industry as \(x_1\) and the amount invested in the kale industry as \(x_2\). The objective is to maximize the return on investment, which is \(0.05x_1 + 0.08x_2\). The constraints are:
1. Total investment: \(x_1 + x_2 \leq 300,000\)
2. Minimum investment in avocado industry: \(x_1 \geq 0.3 \times 300,000\)
3. Maximum investment in kale industry: \(x_2 \leq 0.6 \times 300,000\)
4. Non-negativity: \(x_1 \geq 0, x_2 \geq 0\)

## Step 2: Convert the problem into a symbolic representation

The symbolic representation can be described as:
- Variables: \(x_1\) for the amount invested in the avocado industry, \(x_2\) for the amount invested in the kale industry.
- Objective function: Maximize \(0.05x_1 + 0.08x_2\)
- Constraints:
  1. \(x_1 + x_2 \leq 300,000\)
  2. \(x_1 \geq 90,000\)
  3. \(x_2 \leq 180,000\)
  4. \(x_1, x_2 \geq 0\)

In a JSON-like format for clarity:
```json
{
'sym_variables': [('x1', 'avocado industry investment'), ('x2', 'kale industry investment')],
'objective_function': '0.05*x1 + 0.08*x2',
'constraints': [
    'x1 + x2 <= 300000',
    'x1 >= 90000',
    'x2 <= 180000',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## Step 3: Implement the problem in Gurobi Python

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

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(lb=0, name="avocado_investment")
    x2 = model.addVar(lb=0, name="kale_investment")

    # Objective function: Maximize 0.05*x1 + 0.08*x2
    model.setObjective(0.05*x1 + 0.08*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 300000, name="total_investment")
    model.addConstr(x1 >= 90000, name="min_avocado_investment")
    model.addConstr(x2 <= 180000, name="max_kale_investment")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment in avocado industry: $", x1.varValue)
        print("Optimal investment in kale industry: $", x2.varValue)
        print("Maximum return: ", 0.05*x1.varValue + 0.08*x2.varValue)
    else:
        print("The model is infeasible")

solve_investment_problem()
```