```python
import argparse
import sys
try:
    from sirf.Gadgetron import AcquisitionData, preprocess_acquisition_data, CoilSensitivityData
    from sirf.Gadgetron import AcquisitionModel, ImageData, Reconstructor
except ImportError as e:
    print("Error importing SIRF modules:", e)
    sys.exit(1)

def conjugate_gradient_reconstruction(acq_data, trajectory, show_plots=False):
    # Compute coil sensitivity maps
    csm = CoilSensitivityData()
    csm.calculate(acq_data)

    # Set up the acquisition model
    acq_model = AcquisitionModel()
    acq_model.set_up(acq_data, csm)

    # Perform backward projection to get an initial estimate
    img_init = acq_model.backward(acq_data)

    # Define symmetrical operator for cg-optimisation
    # Assuming the operator is defined elsewhere or using a placeholder here
    operator = None  # Placeholder for the symmetrical operator

    # Implement the iterative reconstruction using Conjugate Gradient method
    # This is a simplified representation, assuming the CG method is defined elsewhere
    img_recon = img_init.conjugate_gradient(operator, acq_data)

    if show_plots:
        img_recon.show(title="Reconstructed Image")

    return img_recon

def main():
    parser = argparse.ArgumentParser(description="Iterative reconstruction with radial phase encoding (RPE) data using SIRF.")
    parser.add_argument("--raw_data_file", type=str, required=True, help="Path to the raw k-space data file.")
    parser.add_argument("--data_path", type=str, required=True, help="Path to the data files.")
    parser.add_argument("--output_file", type=str, required=True, help="Output file for simulated data.")
    parser.add_argument("--engine", type=str, choices=['Gadgetron', 'STIR'], required=True, help="Reconstruction engine.")
    parser.add_argument("--run_recon", action='store_true', help="Run reconstruction if non-cartesian code was compiled.")
    parser.add_argument("--trajectory", type=str, choices=['cartesian', 'radial', 'goldenangle', 'grpe'], required=True, help="Type of trajectory.")
    parser.add_argument("--show_plots", action='store_true', help="Whether to show plots.")
    args = parser.parse_args()

    # Import the necessary engine module based on the specified engine option
    if args.engine == 'Gadgetron':
        from sirf.Gadgetron import AcquisitionData
    elif args.engine == 'STIR':
        from sirf.STIR import AcquisitionData
    else:
        print("Unsupported engine:", args.engine)
        sys.exit(1)

    try:
        # Locate the k-space raw data file
        raw_data_file = args.raw_data_file

        # Read the acquisition data from an HDF file
        acq_data = AcquisitionData(raw_data_file)

        # Pre-process the acquisition data
        processed_acq_data = preprocess_acquisition_data(acq_data)

        # Set the trajectory
        processed_acq_data.set_data_trajectory(args.trajectory)

        # Sort the processed acquisition data
        sorted_acq_data = processed_acq_data.sort()

        if args.run_recon:
            # Perform the reconstruction
            reconstructed_image = conjugate_gradient_reconstruction(sorted_acq_data, args.trajectory, args.show_plots)
            reconstructed_image.write(args.output_file)
    except Exception as e:
        print("An error occurred during execution:", e)
        sys.exit(1)

if __name__ == "__main__":
    main()
```