To solve this optimization problem, we need to define the decision variables, objective function, and constraints. Let's break down the problem:

- Decision variables: 
  - \(x_1\): Amount invested in the avocado industry.
  - \(x_2\): Amount invested in the kale industry.

- Objective function: Maximize the total return from both investments.
  - Return from avocado industry: \(0.05x_1\)
  - Return from kale industry: \(0.08x_2\)
  - Total return: \(0.05x_1 + 0.08x_2\)

- Constraints:
  - Total investment constraint: \(x_1 + x_2 = 300,000\)
  - Minimum investment in avocado industry: \(x_1 \geq 0.30 \times 300,000 = 90,000\)
  - Maximum investment in kale industry: \(x_2 \leq 0.60 \times 300,000 = 180,000\)
  - Non-negativity constraint: \(x_1, x_2 \geq 0\)

Given these definitions, we can formulate the optimization problem as a linear program and solve it using Gurobi in Python.

```python
from gurobipy import *

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

# Define decision variables
x1 = m.addVar(lb=0, name="Avocado_Investment")
x2 = m.addVar(lb=0, name="Kale_Investment")

# Define the objective function: Maximize total return
m.setObjective(0.05*x1 + 0.08*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 == 300000, "Total_Investment")
m.addConstr(x1 >= 90000, "Min_Avocado_Investment")
m.addConstr(x2 <= 180000, "Max_Kale_Investment")

# Optimize the model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Avocado Investment: {x1.x}")
    print(f"Kale Investment: {x2.x}")
    print(f"Total Return: {0.05*x1.x + 0.08*x2.x}")
else:
    print("No optimal solution found.")
```