To solve Miles's investment problem, we first need to define the decision variables and the objective function. Let's denote:

- $x_f$ as the amount invested in the floral industry.
- $x_h$ as the amount invested in the healthcare industry.

The objective is to maximize the total profit from both investments. 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 written as:

Maximize: $1.3x_f + 1.5x_h$

The constraints are:
1. The total amount invested cannot exceed $10,000.
2. At least 25% of all the money invested must be in the floral industry.
3. At least $2000 must be invested in the healthcare industry.

These constraints can be written as:
1. $x_f + x_h \leq 10000$
2. $x_f \geq 0.25(x_f + x_h)$
3. $x_h \geq 2000$

Additionally, we have non-negativity constraints since the amount invested cannot be negative:
- $x_f \geq 0$
- $x_h \geq 0$

However, constraint (2) can be simplified by distributing the 0.25:
- $x_f \geq 0.25x_f + 0.25x_h$
- $0.75x_f \geq 0.25x_h$
- $3x_f \geq x_h$

But for clarity and to maintain the original constraint's intent directly, we'll keep it in its original form in the code.

Now, let's translate this problem into Gurobi code:

```python
from gurobipy import *

# Create a model
m = Model("Miles_Investment")

# Define the decision variables
x_f = m.addVar(name="floral_investment", lb=0)
x_h = m.addVar(name="healthcare_investment", lb=0)

# Define the objective function
m.setObjective(1.3*x_f + 1.5*x_h, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x_f + x_h <= 10000, name="total_investment")
m.addConstr(x_f >= 0.25*(x_f + x_h), name="floral_percentage")
m.addConstr(x_h >= 2000, name="healthcare_minimum")

# Optimize the model
m.optimize()

# Print the results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Floral Investment: {x_f.x}")
    print(f"Healthcare Investment: {x_h.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```