To solve this optimization problem, we first need to translate the natural language description into a symbolic representation. Let's denote the amount invested in the trust as `x1` and the amount invested in the savings account as `x2`.

The objective is to maximize the return on investment, which can be calculated as the sum of the returns from each investment. The return from the trust is 2% of `x1`, and the return from the savings account is 3% of `x2`. Therefore, the objective function can be represented algebraically as:

`0.02*x1 + 0.03*x2`

The constraints are:
- The total amount invested cannot exceed $60,000: `x1 + x2 <= 60000`
- At least 15% of the investment must be placed in the trust: `x1 >= 0.15*60000`
- At most 80% of the investment can be placed in the savings account: `x2 <= 0.8*60000`
- The amounts invested cannot be negative: `x1 >= 0`, `x2 >= 0`

Thus, the symbolic representation of the problem is:
```json
{
    'sym_variables': [('x1', 'amount invested in trust'), ('x2', 'amount invested in savings account')],
    'objective_function': '0.02*x1 + 0.03*x2',
    'constraints': ['x1 + x2 <= 60000', 'x1 >= 0.15*60000', 'x2 <= 0.8*60000', 'x1 >= 0', 'x2 >= 0']
}
```

To find the solution using Gurobi in Python, we'll set up and solve the linear programming problem as follows:

```python
from gurobipy import *

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

# Add variables to the model
x1 = m.addVar(lb=0, name="trust")
x2 = m.addVar(lb=0, name="savings")

# Set the objective function
m.setObjective(0.02*x1 + 0.03*x2, GRB.MAXIMIZE)

# Add constraints to the model
m.addConstr(x1 + x2 <= 60000, "total_investment")
m.addConstr(x1 >= 0.15*60000, "min_trust")
m.addConstr(x2 <= 0.8*60000, "max_savings")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Amount invested in trust: {x1.x}")
    print(f"Amount invested in savings account: {x2.x}")
    print(f"Maximum return on investment: {m.objVal}")
else:
    print("No optimal solution found")
```