## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the amount invested in the airline industry.
- $x_2$ represents the amount invested in the railway industry.

## Step 2: Formulate the objective function
The objective function is to maximize the profit. Given that each dollar invested in the airline industry yields a $0.30 profit and each dollar invested in the railway industry yields a $0.10 profit, the objective function can be written as:
\[ \text{Maximize:} \quad 0.30x_1 + 0.10x_2 \]

## 3: Define the constraints
The constraints based on the problem description are:
1. The total investment should not exceed $50,000: $x_1 + x_2 \leq 50000$
2. A minimum of $10,000 must be invested in the railway industry: $x_2 \geq 10000$
3. At least 25% of all money invested must be in the airline industry: $x_1 \geq 0.25(x_1 + x_2)$

## 4: Simplify the constraints
Simplifying the third constraint:
\[ x_1 \geq 0.25x_1 + 0.25x_2 \]
\[ 0.75x_1 \geq 0.25x_2 \]
\[ 3x_1 \geq x_2 \]

## 5: Symbolic representation
The symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'airline industry investment'), ('x2', 'railway industry investment')],
    'objective_function': '0.30*x1 + 0.10*x2',
    'constraints': [
        'x1 + x2 <= 50000',
        'x2 >= 10000',
        '3*x1 >= x2',
        'x1 >= 0',  # Non-negativity constraint
        'x2 >= 0'   # Non-negativity constraint
    ]
}
```

## 6: Gurobi code
Now, let's write the Gurobi code in Python to solve this linear programming problem:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="airline_investment", lb=0)
    x2 = model.addVar(name="railway_investment", lb=0)

    # Objective function: Maximize 0.30*x1 + 0.10*x2
    model.setObjective(0.30 * x1 + 0.10 * x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 50000, name="total_investment")
    model.addConstr(x2 >= 10000, name="railway_min_investment")
    model.addConstr(3 * x1 >= x2, name="airline_min_percentage")

    # Optimize the model
    model.optimize()

    # Print the results
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Airline Investment: ${x1.varValue:.2f}")
        print(f"Railway Investment: ${x2.varValue:.2f}")
        print(f"Max Profit: ${model.objVal:.2f}")
    else:
        print("The model is infeasible.")

solve_investment_problem()
```