## Symbolic Representation

To solve the given problem, we first need to convert the natural language description into a symbolic representation.

Let's define the variables:
- $x_1$ : dollars invested in the fishing industry
- $x_2$ : dollars invested in the education industry

The objective is to maximize profit. Given that each dollar invested in the fishing industry yields a $1.30 profit and each dollar invested in the education industry yields a $2.10 profit, the objective function can be written as:

Maximize: $1.30x_1 + 2.10x_2$

The constraints are:
1. Total investment should not exceed $20,000: $ $x_1 + x_2 \leq 20000$
2. Minimum of $5,000 must be invested in the education industry: $ $x_2 \geq 5000$
3. At least 30% of all money invested must be in the fishing industry: $ $x_1 \geq 0.30(x_1 + x_2)$

Simplifying the third constraint:
$x_1 \geq 0.30x_1 + 0.30x_2$
$0.70x_1 \geq 0.30x_2$
$x_1 \geq \frac{0.30}{0.70}x_2$
$x_1 \geq \frac{3}{7}x_2$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'dollars invested in the fishing industry'), ('x2', 'dollars invested in the education industry')],
    'objective_function': '1.30x1 + 2.10x2',
    'constraints': [
        'x1 + x2 <= 20000',
        'x2 >= 5000',
        'x1 >= (3/7)x2',
        'x1 >= 0', 
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

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

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

    # Objective function: Maximize 1.30x1 + 2.10x2
    model.setObjective(1.30 * x1 + 2.10 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 20000, name="total_investment")
    model.addConstr(x2 >= 5000, name="min_education_investment")
    model.addConstr(x1 >= (3/7) * x2, name="fishing_investment_ratio")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Invest ${x1.x:.2f} in the fishing industry.")
        print(f"Invest ${x2.x:.2f} in the education industry.")
        print(f"Max Profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

luca_investment_problem()
```