## 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 fertilizer company
- $x_2$ : Amount invested in the pesticide company

The objective is to maximize profit. Given that each dollar invested in the fertilizer company yields a $0.14 profit and each dollar invested in the pesticide company yields a $0.15 profit, the objective function can be represented as:

Maximize: $0.14x_1 + 0.15x_2$

The constraints are:
1. Total investment should not exceed $5000: $x_1 + x_2 \leq 5000$
2. At least 50% should be invested in the fertilizer company: $x_1 \geq 0.5(x_1 + x_2)$
3. At least $1000 should be invested in the pesticide company: $x_2 \geq 1000$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Converting to Standard Form

The symbolic representation in a standard form is:

```json
{
    'sym_variables': [('x1', 'fertilizer company'), ('x2', 'pesticide company')],
    'objective_function': '0.14*x1 + 0.15*x2',
    'constraints': [
        'x1 + x2 <= 5000',
        'x1 >= 0.5*(x1 + x2)',
        'x2 >= 1000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code

Now, let's implement this in Gurobi using Python:

```python
import gurobi

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

    # Define variables
    x1 = model.addVar(name="fertilizer_company", lb=0)
    x2 = model.addVar(name="pesticide_company", lb=0)

    # Objective function: Maximize 0.14*x1 + 0.15*x2
    model.setObjective(0.14*x1 + 0.15*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(x1 + x2 <= 5000, name="total_investment")
    model.addConstr(x1 >= 0.5 * (x1 + x2), name="fertilizer_proportion")
    model.addConstr(x2 >= 1000, name="pesticide_minimum")

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal Solution:")
        print(f"Invest in fertilizer company: ${x1.x:.2f}")
        print(f"Invest in pesticide company: ${x2.x:.2f}")
        print(f"Max Profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found.")

solve_investment_problem()
```