## Step 1: Define the symbolic representation of the problem
The problem involves investing in four industries: apple, orange, pear, and banana, with rates of return 5%, 6%, 3%, and 8% respectively. Let's denote the amount invested in each industry as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Identify the objective function
The objective is to maximize the total return on investment, which can be represented as $0.05x_1 + 0.06x_2 + 0.03x_3 + 0.08x_4$.

## 3: List the constraints
1. The total investment is limited to $300,000: $x_1 + x_2 + x_3 + x_4 \leq 300,000$.
2. The amount invested in the banana industry cannot exceed the amount invested in the apple industry: $x_4 \leq x_1$.
3. The amount invested in the orange industry cannot exceed the amount invested in the pear industry: $x_2 \leq x_3$.
4. At most 30% of the total amount can be in the banana industry: $x_4 \leq 0.3(x_1 + x_2 + x_3 + x_4)$.

## 4: Convert the constraints into standard form
1. $x_1 + x_2 + x_3 + x_4 \leq 300,000$
2. $x_1 - x_4 \geq 0$
3. $x_3 - x_2 \geq 0$
4. $0.7x_4 - 0.3x_1 - 0.3x_2 - 0.3x_3 \leq 0$

## 5: Symbolic representation in JSON format
```json
{
'sym_variables': [('x1', 'apple industry'), ('x2', 'orange industry'), ('x3', 'pear industry'), ('x4', 'banana industry')],
'objective_function': '0.05x1 + 0.06x2 + 0.03x3 + 0.08x4',
'constraints': [
'x1 + x2 + x3 + x4 <= 300000',
'x1 - x4 >= 0',
'x3 - x2 >= 0',
'0.7x4 - 0.3x1 - 0.3x2 - 0.3x3 <= 0',
'x1 >= 0', 'x2 >= 0', 'x3 >= 0', 'x4 >= 0'
]
}
```

## 6: 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="apple", lb=0)
    x2 = model.addVar(name="orange", lb=0)
    x3 = model.addVar(name="pear", lb=0)
    x4 = model.addVar(name="banana", lb=0)

    # Objective function
    model.setObjective(0.05*x1 + 0.06*x2 + 0.03*x3 + 0.08*x4, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 + x3 + x4 <= 300000)
    model.addConstr(x1 - x4 >= 0)
    model.addConstr(x3 - x2 >= 0)
    model.addConstr(0.7*x4 - 0.3*x1 - 0.3*x2 - 0.3*x3 <= 0)

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal investment amounts:")
        print(f"Apple: ${x1.varValue:.2f}")
        print(f"Orange: ${x2.varValue:.2f}")
        print(f"Pear: ${x3.varValue:.2f}")
        print(f"Banana: ${x4.varValue:.2f}")
        print(f"Total return: ${model.objVal:.2f}")
    else:
        print("The model is infeasible.")

solve_investment_problem()
```