To solve Steven's investment problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables and formulating the objective function and constraints.

Let's define:
- \(x_1\) as the amount invested in Delta,
- \(x_2\) as the amount invested in Omega.

The objective is to maximize profit. Given that each dollar invested in Delta yields $0.80 profit and each dollar in Omega yields $1.20 profit, the objective function can be written as:
\[ \text{Maximize: } 0.80x_1 + 1.20x_2 \]

The constraints are:
1. The total budget is at most $100,000:
\[ x_1 + x_2 \leq 100,000 \]
2. A minimum of 25% of all money invested must be in Delta:
\[ x_1 \geq 0.25(x_1 + x_2) \]
3. At least $10,000 must be invested in Omega:
\[ x_2 \geq 10,000 \]
4. Non-negativity constraints (since we cannot invest a negative amount):
\[ x_1 \geq 0 \]
\[ x_2 \geq 0 \]

In symbolic representation, this problem can be summarized as:

```json
{
  'sym_variables': [('x1', 'amount invested in Delta'), ('x2', 'amount invested in Omega')],
  'objective_function': '0.80*x1 + 1.20*x2',
  'constraints': [
    'x1 + x2 <= 100000',
    'x1 >= 0.25*(x1 + x2)',
    'x2 >= 10000',
    'x1 >= 0',
    'x2 >= 0'
  ]
}
```

To solve this problem using Gurobi in Python, we can use the following code:

```python
from gurobipy import *

# Create a new model
m = Model("Investment_Problem")

# Add variables
x1 = m.addVar(lb=0, name="Delta_Investment")
x2 = m.addVar(lb=0, name="Omega_Investment")

# Set the objective function
m.setObjective(0.80*x1 + 1.20*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 + x2 <= 100000, "Budget_Constraint")
m.addConstr(x1 >= 0.25*(x1 + x2), "Delta_Minimum_Constraint")
m.addConstr(x2 >= 10000, "Omega_Minimum_Constraint")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Delta Investment: {x1.x}")
    print(f"Omega Investment: {x2.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```