## 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:
- $x_1$ : Amount invested in the floral industry
- $x_2$ : Amount invested in the healthcare industry

The objective is to maximize the profit. Given that each dollar invested in the floral industry yields a $1.3 profit and each dollar invested in the healthcare industry yields a $1.5 profit, the objective function can be represented as:

Maximize: $1.3x_1 + 1.5x_2$

The constraints based on the problem description are:
1. Total investment not exceeding $10000: $x_1 + x_2 \leq 10000$
2. At least 25% of all the money invested must be in the floral industry: $x_1 \geq 0.25(x_1 + x_2)$
3. At least $2000 must be invested in the healthcare industry: $x_2 \geq 2000$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'Amount invested in the floral industry'), ('x2', 'Amount invested in the healthcare industry')],
    'objective_function': 'Maximize: 1.3*x1 + 1.5*x2',
    'constraints': [
        'x1 + x2 <= 10000',
        'x1 >= 0.25*(x1 + x2)',
        'x2 >= 2000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## 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="floral_investment", lb=0)
    x2 = model.addVar(name="healthcare_investment", lb=0)

    # Objective function: Maximize 1.3*x1 + 1.5*x2
    model.setObjective(1.3 * x1 + 1.5 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 10000, name="total_investment")
    model.addConstr(x1 >= 0.25 * (x1 + x2), name="floral_percentage")
    model.addConstr(x2 >= 2000, name="healthcare_minimum")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Floral Industry Investment: ${x1.x:.2f}")
        print(f"Healthcare Industry Investment: ${x2.x:.2f}")
        print(f"Maximum Profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible.")

solve_investment_problem()
```